Datasets:

semeru

/

Galeras-3k_deduped

like 0

Follow

Semeru Lab 7

salt/modules/win_certutil.py

Fix win_certutil module to handle paths with spaces

salt

Python

win_certutil.py

def __virtual__(): if salt.utils.platform.is_windows(): return __virtualname__ return False, "Module win_certutil: module only works on Windows systems."

53b3ebc92648c2081c58865713b50a2859ae8310

https://github.com/saltstack/salt.git

def __virtual__(): if salt.utils.platform.is_windows(): return __virtualname__

__virtual__

tests/sentry/api/endpoints/test_user_index.py

ref(tests): Remove `get_valid_response()` (#34822)

sentry

Python

test_user_index.py

def test_email_query(self): response = self.get_success_response(qs_params={"query": "email:bar@example.com"}) assert len(response.data) == 1 assert response.data[0]["id"] == str(self.superuser.id) response = self.get_success_response(qs_params={"query": "email:foobar"}) assert len(response.data) == 0

096b5511e244eecd8799b2a0324655207ce8985e

https://github.com/getsentry/sentry.git

def test_email_query(self): response = self.get_success_response(qs_params={"query": "email:bar@example.com"}) assert len(response.data) == 1 assert response.data[0]["id"] == str(self.superuser.id) response = s

test_email_query

rotate_string.py

refactor: clean code Signed-off-by: slowy07 <slowy.arfy@gmail.com>

Python

Python

rotate_string.py

def circular_rotate(s): s = list(s) idx = 0 mid = len(s) // 2 for i in reversed(range(mid, len(s))): s[idx], s[i] = s[i], s[idx] idx += 1 return s s = "aditya" print("".join(circular_rotate(s)))

f0af0c43340763724f139fa68aa1e5a9ffe458b4

https://github.com/geekcomputers/Python.git

def circular_rotate(s): s = list(s) idx = 0 mid = len(s) // 2 for i in reversed(range(mid, len(s))): s[idx], s[i] = s[i], s[

circular_rotate

tests/gamestonk_terminal/economy/test_economy_controller.py

Tests : Economy + Conftest (#1260) * Updating tests : economy * Updating tests : removing breaklines * Updating tests : economy * Updating tests : conftest * Updating tests : economy

OpenBBTerminal

Python

test_economy_controller.py

def test_switch(an_input, expected_queue): controller = economy_controller.EconomyController(queue=None) queue = controller.switch(an_input=an_input) assert queue == expected_queue @pytest.mark.vcr(record_mode="none")

683a8bdd83c1b931df111a5b2b8b19350930b73a

@pytest.mark.vcr(record_mode="none")

https://github.com/OpenBB-finance/OpenBBTerminal.git

def test_switch(an_input, expected_queue):

test_switch

airflow/decorators/base.py

Implement mapped value unpacking (#21641)

airflow

Python

base.py

def get_serialized_fields(cls): # The magic super() doesn't work here, so we use the explicit form. # Not using super(..., cls) to work around pyupgrade bug. sup = super(DecoratedMappedOperator, DecoratedMappedOperator) return sup.get_serialized_fields() | {"mapped_op_kwargs"}

46a337c8cda6fcc515fffe9a4e4cc324edaefa0a

https://github.com/apache/airflow.git

def get_serialized_fields(cls): # The magic super() doesn't work here, so we use the explicit form. # Not using super(..., cls) to work around pyupgrade bug. sup = super(DecoratedMappedOperator, DecoratedMappedOperator) return sup

get_serialized_fields

dask/dataframe/io/tests/test_parquet.py

Warn users that `engine='auto'` will change in future (#8907) * Warn user that `engine='auto'` will change in future Adds a warning to `read_parquet` and `to_parquet` that the meaning of `engine='auto'` will change in the future (switching to using `pyarrow` if it is installed, and falling back to `fastparquet`). This warning is only raised for users that have both libraries installed and have `engine='auto'`. Users without both libraries installed or that already specify an engine will not see this warning. * Fixup failing test * Only warn if backend-specific options are passed We now warn if: - `engine='auto'` - Both `pyarrow` and `fastparquet` are installed - Backend specific options are provided

dask

Python

test_parquet.py

def test_optimize_and_not(tmpdir, engine): path = os.path.join(tmpdir, "path.parquet") df = pd.DataFrame( {"a": [3, 4, 2], "b": [1, 2, 4], "c": [5, 4, 2], "d": [1, 2, 3]}, index=["a", "b", "c"], ) df.to_parquet(path, engine=engine) df2 = dd.read_parquet(path, engine=engine) df2a = df2["a"].groupby(df2["c"]).first().to_delayed() df2b = df2["b"].groupby(df2["c"]).first().to_delayed() df2c = df2[["a", "b"]].rolling(2).max().to_delayed() df2d = df2.rolling(2).max().to_delayed() (result,) = dask.compute(df2a + df2b + df2c + df2d) expected = [ dask.compute(df2a)[0][0], dask.compute(df2b)[0][0], dask.compute(df2c)[0][0], dask.compute(df2d)[0][0], ] for a, b in zip(result, expected): assert_eq(a, b) @write_read_engines()

bfc76afdcdc43c575a10ffefda94aaba424fe347

@write_read_engines()

https://github.com/dask/dask.git

def test_optimize_and_not(tmpdir, engine): path = os.path.join(tmpd

test_optimize_and_not

mindsdb/interfaces/model/model_controller.py

keep del company_id

mindsdb

Python

model_controller.py

def delete_model_version(self, models): if len(models) == 0: raise Exception(f"Version to delete is not found") for model in models: model_record = get_model_record( name=model['NAME'], project_name=model['PROJECT'], version=model['VERSION'] ) if model_record.active: raise Exception(f"Can't remove active version: f{model['PROJECT']}.{model['NAME']}.{model['VERSION']}") is_cloud = self.config.get('cloud', False) if is_cloud: model_record.deleted_at = dt.datetime.now() else: db.session.delete(model_record) modelStorage = ModelStorage(model_record.id) modelStorage.delete() db.session.commit()

402b28c300dbca149db75439c358f811ea1d36ac

https://github.com/mindsdb/mindsdb.git

def delete_model_version(self, models): if len(models) == 0: raise Exception(f"Version to dele

delete_model_version

dashboard/modules/job/tests/test_job_manager.py

[CI] Format Python code with Black (#21975) See #21316 and #21311 for the motivation behind these changes.

ray

Python

test_job_manager.py

async def test_unknown_job(self, job_manager): with pytest.raises(RuntimeError, match="Job 'unknown' does not exist."):

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

https://github.com/ray-project/ray.git

async def test_unknown_job(self, job_manager): with pytest.raises(RuntimeError, match="Job 'unknown' does not exist."):

test_unknown_job

tests/ludwig/utils/test_dataframe_utils.py

Ensure no ghost ray instances are running in tests (#2607) * Add test fixtures to prevent double initialization * contain dataframe utils test in ray test fixture * add fixture for training determinism test * fix typo in docstring

ludwig

Python

test_dataframe_utils.py

def test_to_numpy_dataset_with_pandas_backend_mismatch(ray_cluster_2cpu): pd_df = pd.DataFrame([[1, 2, 3]], columns=["col1", "col2", "col3"]) ray_backend = create_backend("ray") with pytest.raises(AttributeError): to_numpy_dataset(pd_df, backend=ray_backend)

0f509327aed9f9ec687543e1b851f66061603375

https://github.com/ludwig-ai/ludwig.git

def test_to_numpy_dataset_with_pandas_backend_mismatch(ray_cluster_2cpu): pd_df = pd.DataFrame([[

test_to_numpy_dataset_with_pandas_backend_mismatch

pandas/core/arrays/interval.py

REF: implement LossySetitemError (#45672)

pandas

Python

interval.py

def _validate_listlike(self, value): # list-like of intervals try: array = IntervalArray(value) self._check_closed_matches(array, name="value") value_left, value_right = array.left, array.right except TypeError as err: # wrong type: not interval or NA msg = f"'value' should be an interval type, got {type(value)} instead." raise TypeError(msg) from err try: self.left._validate_fill_value(value_left) except (LossySetitemError, TypeError) as err: msg = ( "'value' should be a compatible interval type, " f"got {type(value)} instead." ) raise TypeError(msg) from err return value_left, value_right

9a98aca4b57af277b7747e402029bd57088cba2c

https://github.com/pandas-dev/pandas.git

def _validate_listlike(self, value): # list-like of intervals try: array = Inter

_validate_listlike

saleor/payment/tests/test_utils.py

Refactor voucher and shipping amount for payment lines data

saleor

Python

test_utils.py

def test_create_refund_data_order_lines(order_with_lines, refund_shipping_costs): # given order_lines = order_with_lines.lines.all() order_refund_lines = [ OrderLineInfo(line=(line := order_lines[0]), quantity=2, variant=line.variant), OrderLineInfo(line=(line := order_lines[1]), quantity=1, variant=line.variant), ] fulfillment_refund_lines = [] # when refund_data = create_refund_data( order_with_lines, order_refund_lines, fulfillment_refund_lines, refund_shipping_costs, ) # then assert refund_data.lines == { line.variant_id: line.quantity - refund_line.quantity for line, refund_line in zip(order_lines, order_refund_lines) } assert refund_data.shipping == refund_shipping_costs @pytest.mark.parametrize("refund_shipping_costs", [True, False])

f365f77eb90579cb0dcccc24735f82e039b71f62

@pytest.mark.parametrize("refund_shipping_costs", [True, False])

https://github.com/saleor/saleor.git

def test_create_refund_data_order_lines(order_with_lines, refund_shipping_costs): # given order_lines = order_with_lines.lines.all() order_refund_lines = [ OrderLineInfo(line=(line := order_lines[0]), quantity=2, variant=

test_create_refund_data_order_lines

mindsdb/api/mysql/mysql_proxy/mysql_proxy.py

cast column names on create predictor

mindsdb

Python

mysql_proxy.py

def answer_create_predictor(self, statement): struct = { 'predictor_name': statement.name.parts[-1], 'integration_name': statement.integration_name.parts[-1], 'select': statement.query_str, 'predict': [x.parts[-1] for x in statement.targets] } if len(struct['predict']) > 1: raise Exception("Only one field can be in 'PREDICT'") if statement.using is not None: struct['using'] = statement.using if statement.datasource_name is not None: struct['datasource_name'] = statement.datasource_name.parts[-1] if statement.order_by is not None: struct['order_by'] = [x.field.parts[-1] for x in statement.order_by] if len(struct['order_by']) > 1: raise Exception("Only one field can be in 'OPRDER BY'") if statement.group_by is not None: struct['group_by'] = [x.parts[-1] for x in statement.group_by] if statement.window is not None: struct['window'] = statement.window if statement.horizon is not None: struct['horizon'] = statement.horizon model_interface = self.session.model_interface data_store = self.session.data_store predictor_name = struct['predictor_name'] integration_name = struct['integration_name'] if integration_name.lower().startswith('datasource.'): ds_name = integration_name[integration_name.find('.') + 1:] ds = data_store.get_datasource_obj(ds_name, raw=True) ds_data = data_store.get_datasource(ds_name) else: if self.session.datasource_interface.get_db_integration(integration_name) is None: raise Exception(f"Unknown integration: {integration_name}") ds_name = struct.get('datasource_name') if ds_name is None: ds_name = data_store.get_vacant_name(predictor_name) ds = data_store.save_datasource(ds_name, integration_name, {'query': struct['select']}) ds_data = data_store.get_datasource(ds_name) timeseries_settings = {} for w in ['order_by', 'group_by', 'window', 'horizon']: if w in struct: timeseries_settings[w] = struct.get(w) kwargs = struct.get('using', {}) if len(timeseries_settings) > 0: if 'timeseries_settings' not in kwargs: kwargs['timeseries_settings'] = timeseries_settings else: if isinstance(kwargs.get('timeseries_settings'), str): kwargs['timeseries_settings'] = json.loads(kwargs['timeseries_settings']) kwargs['timeseries_settings'].update(timeseries_settings) ds_column_names = [x['name'] for x in ds_data['columns']] try: predict = self._check_predict_columns(struct['predict'], ds_column_names) except Exception: data_store.delete_datasource(ds_name) raise

4f36633784a0b0e8a043bbd71d5a1f2ae8f584ed

https://github.com/mindsdb/mindsdb.git

def answer_create_predictor(self, statement): struct = { 'predictor_name': statement.name.parts[-1], 'integration_name': statement.integration_name.parts[-1], 'select': statement.query_str, 'predict': [x.parts[-1] for x in statement.targets] } if len(struct['predict']) > 1: raise Exception("Only one field can be in 'PREDICT'") if statement.using is not None: struct['using'] = statement.using if statement.datasource_name is not None: struct['datasource_name'] = statement.datasource_name.parts[-1] if statement.order_by is not None: struct['order_by'] = [x.field.parts[-1] for x in statement.order_by] if len(struct['order_by']) > 1: raise Exception("Only one field can be in 'OPRDER BY'") if statemen

answer_create_predictor

tests/backends/sqlite/tests.py

Refs #33476 -- Reformatted code with Black.

django

Python

tests.py

def test_distinct_aggregation_multiple_args_no_distinct(self): # Aggregate functions accept multiple arguments when DISTINCT isn't # used, e.g. GROUP_CONCAT().

9c19aff7c7561e3a82978a272ecdaad40dda5c00

https://github.com/django/django.git

def test_distinct_aggregation_multiple_args_no_distinct(self): # Aggregate

test_distinct_aggregation_multiple_args_no_distinct

packages/syft/src/syft/core/node/common/node_service/tff/tff_service.py

fixed security tests

PySyft

Python

tff_service.py

def aux_recursive_od2d(dit): new_dict = {} for key in dit: if type(dit[key]) == collections.OrderedDict: new_elem = aux_recursive_od2d(dit[key]) new_dict[key] = new_elem else: new_dict[key] = dit[key] return new_dict

5724f9c7db0a840c6c209d1d02d03c24ddf59d35

https://github.com/OpenMined/PySyft.git

def aux_recursive_od2d(dit): new_dict = {} for key in dit: if type(dit[key]) == collections.OrderedDict: new_elem = aux_recursive_od2d(dit[key]) new_dict[key] = new_elem

aux_recursive_od2d

python/ray/tune/examples/cifar10_pytorch.py

[air] update documentation to use `session.report` (#26051) Update documentation to use `session.report`. Next steps: 1. Update our internal caller to use `session.report`. Most importantly, CheckpointManager and DataParallelTrainer. 2. Update `get_trial_resources` to use PGF notions to incorporate the requirement of ResourceChangingScheduler. @Yard1 3. After 2 is done, change all `tune.get_trial_resources` to `session.get_trial_resources` 4. [internal implementation] remove special checkpoint handling logic from huggingface trainer. Optimize the flow for checkpoint conversion with `session.report`. Co-authored-by: Antoni Baum <antoni.baum@protonmail.com>

ray

Python

cifar10_pytorch.py

def train_cifar(config): net = Net(config["l1"], config["l2"]) device = "cpu" if torch.cuda.is_available(): device = "cuda:0" if torch.cuda.device_count() > 1: net = nn.DataParallel(net) net.to(device) criterion = nn.CrossEntropyLoss() optimizer = optim.SGD(net.parameters(), lr=config["lr"], momentum=0.9) # Load existing checkpoint through `session.get_checkpoint()` API. if session.get_checkpoint(): loaded_checkpoint = session.get_checkpoint() with loaded_checkpoint.as_directory() as loaded_checkpoint_dir: model_state, optimizer_state = torch.load(os.path.join(loaded_checkpoint_dir, "checkpoint.pt")) net.load_state_dict(model_state) optimizer.load_state_dict(optimizer_state) data_dir = os.path.abspath("./data") trainset, testset = load_data(data_dir) test_abs = int(len(trainset) * 0.8) train_subset, val_subset = random_split( trainset, [test_abs, len(trainset) - test_abs]) trainloader = torch.utils.data.DataLoader( train_subset, batch_size=int(config["batch_size"]), shuffle=True, num_workers=8) valloader = torch.utils.data.DataLoader( val_subset, batch_size=int(config["batch_size"]), shuffle=True, num_workers=8) for epoch in range(10): # loop over the dataset multiple times running_loss = 0.0 epoch_steps = 0 for i, data in enumerate(trainloader, 0): # get the inputs; data is a list of [inputs, labels] inputs, labels = data inputs, labels = inputs.to(device), labels.to(device) # zero the parameter gradients optimizer.zero_grad() # forward + backward + optimize outputs = net(inputs) loss = criterion(outputs, labels) loss.backward() optimizer.step() # print statistics running_loss += loss.item() epoch_steps += 1 if i % 2000 == 1999: # print every 2000 mini-batches print("[%d, %5d] loss: %.3f" % (epoch + 1, i + 1, running_loss / epoch_steps)) running_loss = 0.0 # Validation loss val_loss = 0.0 val_steps = 0 total = 0 correct = 0 for i, data in enumerate(valloader, 0): with torch.no_grad(): inputs, labels = data inputs, labels = inputs.to(device), labels.to(device) outputs = net(inputs) _, predicted = torch.max(outputs.data, 1) total += labels.size(0) correct += (predicted == labels).sum().item() loss = criterion(outputs, labels) val_loss += loss.cpu().numpy() val_steps += 1 # Here we save a checkpoint. It is automatically registered with # Ray Tune and will potentially be accessed through in ``session.get_checkpoint()`` # in future iterations. # Note to save a file like checkpoint, you still need to put it under a directory # to construct an AIR checkpoint. os.makedirs("my_model", exist_ok=True) # ok to overwrite the previous one. path = os.path.join("my_model", "checkpoint.pt") torch.save( (net.state_dict(), optimizer.state_dict()), path) checkpoint = Checkpoint.from_directory("my_model") session.report({"loss": (val_loss / val_steps), "accuracy": correct / total}, checkpoint=checkpoint) print("Finished Training") # __train_end__ # __test_acc_begin__

ac831fded416381ad3c7fe2ba135eaa4aaab5879

https://github.com/ray-project/ray.git

def train_cifar(config): net = Net(config["l1"], config["l2"]) device = "cpu" if torch.cuda.is_available(): device = "cuda:0" if torch.cuda.device_count() > 1: net = nn.DataParallel(net) net.to(device) criterion = nn.CrossEntropyLoss() optimizer = optim.SGD(net.parameters(), lr=config["lr"], momentum=0.9) # Load existing checkpoint through `session.get_checkpoint()` API. if session.get_checkpoint(): loaded_checkpoint = session.get_checkpoint() with loaded_checkpoint.as_directory() as loaded_checkpoint_dir: model_state, optimizer_state = torch.load(os.path.join(loaded_checkpoint_dir, "checkpoint.pt")) net.load_state_dict(model_state) optimizer.load_state_dict(optimizer_state) data_dir = os.path.abspath("./data") trainset, testset = load_data(data_dir) test_abs = int(len(trainset) * 0.8) train_subset, val_subset = random_split( trainset, [test_abs, len(trainset) - test_abs]) trainloader = torch.utils.data.DataLoader( train_subset, batch_size=int(config["batch_size"]), shuffle=True, num_workers=8) valloader = torch.utils.data.DataLoader( val_subset, batch_size=int(config["batch_size"]), shuffle=True, num_workers=8) for epoch in range(10): # loop over the dataset multiple times running_loss = 0.0 epoch_steps = 0 for i, data in enumerate(trainloader, 0): # get the inputs; data is a list of [inputs, labels] inputs, labels = data inputs, labels = inputs.to(device), labels.to(device) # zero the parameter gradients optimizer.zero_grad() # forward + backward + optimize

train_cifar

tests/pipelines/test_pipelines_common.py

feat: add pipeline registry abstraction (#17905) * feat: add pipeline registry abstraction - added `PipelineRegistry` abstraction - updates `add_new_pipeline.mdx` (english docs) to reflect the api addition - migrate `check_task` and `get_supported_tasks` from transformers/pipelines/__init__.py to transformers/pipelines/base.py#PipelineRegistry.{check_task,get_supported_tasks} Signed-off-by: Aaron Pham <29749331+aarnphm@users.noreply.github.com> * fix: update with upstream/main chore: Apply suggestions from sgugger's code review Signed-off-by: Aaron Pham <29749331+aarnphm@users.noreply.github.com> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * chore: PR updates - revert src/transformers/dependency_versions_table.py from upstream/main - updates pipeline registry to use global variables Signed-off-by: Aaron Pham <29749331+aarnphm@users.noreply.github.com> * tests: add tests for pipeline registry Signed-off-by: Aaron Pham <29749331+aarnphm@users.noreply.github.com> * tests: add test for output warning. Signed-off-by: Aaron Pham <29749331+aarnphm@users.noreply.github.com> * chore: fmt and cleanup unused imports Signed-off-by: Aaron Pham <29749331+aarnphm@users.noreply.github.com> * fix: change imports to top of the file and address comments Signed-off-by: Aaron Pham <29749331+aarnphm@users.noreply.github.com> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

transformers

Python

test_pipelines_common.py

def test_register_pipeline(self): custom_text_classification = { "impl": CustomPipeline, "tf": (), "pt": (AutoModelForSequenceClassification,), "default": {"model": {"pt": "hf-internal-testing/tiny-random-distilbert"}}, "type": "text", } PIPELINE_REGISTRY.register_pipeline("custom-text-classification", custom_text_classification) assert "custom-text-classification" in PIPELINE_REGISTRY.get_supported_tasks() task_def, _ = PIPELINE_REGISTRY.check_task("custom-text-classification") self.assertEqual(task_def, custom_text_classification) self.assertEqual(task_def["type"], "text") self.assertEqual(task_def["impl"], CustomPipeline)

49cd736a288a315d741e5c337790effa4c9fa689

https://github.com/huggingface/transformers.git

def test_register_pipeline(self): custom_text_classification = { "impl": CustomPipeline, "tf": (), "pt": (AutoModelForSequenceClassification,), "default": {"model": {"pt": "hf-internal-testing/tiny-random-d

test_register_pipeline

airbyte-integrations/connectors/source-chargebee/source_chargebee/source.py

:tada: Source Chargebee: add credit note model (#10795) * feat(chargebee) add credit note model * fix(airbyte): update version Dockerfile * fix(airbyte): update version Dockerfile v2

airbyte

Python

source.py

def streams(self, config) -> List[Stream]: # Configure the Chargebee Python SDK chargebee.configure(api_key=config["site_api_key"], site=config["site"]) kwargs = {"start_date": config["start_date"]} product_catalog_version = config["product_catalog"] # Below streams are suitable for both `Product Catalog 1.0` and `Product Catalog 2.0`. common_streams = [ Coupon(**kwargs), CreditNote(**kwargs), Customer(**kwargs), Event(**kwargs), Invoice(**kwargs), Order(**kwargs), Subscription(**kwargs), Transaction(**kwargs), ] if product_catalog_version == "1.0": # Below streams are suitable only for `Product Catalog 1.0`. product_catalog_v1_streams = [ Addon(**kwargs), Plan(**kwargs), ] return common_streams + product_catalog_v1_streams # Below streams are suitable only for `Product Catalog 2.0`. product_catalog_v2_streams = [ Item(**kwargs), ItemPrice(**kwargs), AttachedItem(**kwargs), ] return common_streams + product_catalog_v2_streams

16dfae0df7290392e5f0754731ae53ae7b7f939d

https://github.com/airbytehq/airbyte.git

def streams(self, config) -> List[Stream]: # Configure the Chargebee Python SDK chargebee.configure(api_key=config["site_api_key"], site=config["site"]) kwargs = {"start_date": config["start_date"]} product_catalog_version = config["product_catalog"] # Below streams are suitable for both `Product Catalog 1.0` and `Product Catalog 2.0`. common_streams = [ Coupon(**kwargs), CreditNote(**kwargs), Customer(**kwargs), Event(**kwargs), Invoice(**kwargs), Order(**kwargs), Subscription(**kwargs), Transaction(**kwargs), ] if product_catalog_version == "1.0": # Below streams are suitable only for `Product Catalog 1.0`. product_catalog_v1_s

streams

saleor/plugins/webhook/tests/subscription_webhooks/test_create_deliveries_for_subscriptions_payments.py

GraphQL subscription support for synchronous webhook events (#9763) * WIP add sync webhooks subscription payload handling * add tests, fix minor things * update schema * remove unneeded code * add fix for circular field resolve * fix-filter-shipping-methods-payload * added_in added to desription * add missing types * revert refactor, precommit issues * fixes after review * cosmetix fixes post-review * subscription types description fixes * remove unneeded description from PaymentBase * add validation for creating webhook with two top level fields, add tests for shippingListMethodsForCheckout * add docstring, refactor prevent_sync_event_circular_wuery wrapper * fix docstring of revent_sync_event_circular_query * fix linters

saleor

Python

test_create_deliveries_for_subscriptions_payments.py

def test_payment_confirm(payment, subscription_payment_confirm_webhook): # given webhooks = [subscription_payment_confirm_webhook] event_type = WebhookEventSyncType.PAYMENT_CONFIRM # when deliveries = create_deliveries_for_subscriptions(event_type, payment, webhooks) # then expected_payload = generate_payment_payload(payment) assert json.loads(deliveries[0].payload.payload) == expected_payload assert len(deliveries) == len(webhooks) assert deliveries[0].webhook == webhooks[0]

8201efcde2d7aacccf3512c544cceea6780a0598

https://github.com/saleor/saleor.git

def test_payment_confirm(payment, subscription_payment_confirm_webhook): # given webhooks = [subscription_payment_confirm_webhoo

test_payment_confirm

tests/pytests/unit/state/test_state_compiler.py

Fix test cases with PermissionError on /var/cache/salt When running the test cases without root permission, some test cases fail: ``` $ python3 -m pytest -ra tests/pytests/unit/state/test_state_compiler.py tests/pytests/unit/test_minion.py [...] FAILED tests/pytests/unit/state/test_state_compiler.py::test_render_requisite_require_disabled - PermissionError: [Errno 13] Permission denied: '/var/cache/salt' FAILED tests/pytests/unit/state/test_state_compiler.py::test_render_requisite_require_in_disabled - PermissionError: [Errno 13] Permission denied: '/var/cache/salt' FAILED tests/pytests/unit/test_minion.py::test_minion_module_refresh - PermissionError: [Errno 13] Permission denied: '/var/cache/salt' FAILED tests/pytests/unit/test_minion.py::test_minion_module_refresh_beacons_refresh - PermissionError: [Errno 13] Permission denied: '/var/cache/salt' ``` Fix these test cases by using a temporary directory as cache directory. Signed-off-by: Benjamin Drung <benjamin.drung@ionos.com>

salt

Python

test_state_compiler.py

def test_render_requisite_require_disabled(tmp_path): with patch("salt.state.State._gather_pillar") as state_patch: high_data = { "step_one": OrderedDict( [ ( "test", [ OrderedDict( [("require", [OrderedDict([("test", "step_two")])])] ), "succeed_with_changes", {"order": 10000}, ], ), ("__sls__", "test.disable_require"), ("__env__", "base"), ] ), "step_two": { "test": ["succeed_with_changes", {"order": 10001}], "__env__": "base", "__sls__": "test.disable_require", }, } minion_opts = salt.config.DEFAULT_MINION_OPTS.copy() minion_opts["cachedir"] = str(tmp_path) minion_opts["disabled_requisites"] = ["require"] state_obj = salt.state.State(minion_opts) ret = state_obj.call_high(high_data) run_num = ret["test_|-step_one_|-step_one_|-succeed_with_changes"][ "__run_num__" ] assert run_num == 0

fae21e4698d9bb45a407345e7dff5ce3b69f799d

https://github.com/saltstack/salt.git

def test_render_requisite_require_disabled(tmp_path): with patch("salt.state.State._gather_pillar") as state_patch: high_data = { "step_one": OrderedDict( [ ( "test", [ OrderedDict( [("require", [OrderedDict([("test", "step_two")])])] ), "succeed_w

test_render_requisite_require_disabled

django/core/checks/security/sessions.py

Refs #33476 -- Reformatted code with Black.

django

Python

sessions.py

def _session_middleware(): return "django.contrib.sessions.middleware.SessionMiddleware" in settings.MIDDLEWARE

9c19aff7c7561e3a82978a272ecdaad40dda5c00

https://github.com/django/django.git

def _session_middleware(): return "d

_session_middleware

mindsdb/integrations/handlers/lightwood_handler/tests/test_lightwood_handler.py

TS tests pass

mindsdb

Python

test_lightwood_handler.py

def test_13_train_ts_predictor_no_gby_hor1(self): query = f if self.test_model_2 not in self.handler.get_tables().data_frame.values: self.handler.native_query(query) else: self.handler.native_query(f"DROP PREDICTOR {self.test_model_2}") self.handler.native_query(query)

74977c69effc89a56080357449d5d337988daab7

https://github.com/mindsdb/mindsdb.git

def test_13_train_ts_predictor_no_gby_hor1(self): query = f if self.test_

test_13_train_ts_predictor_no_gby_hor1

pandas/core/exchange/column.py

ENH: Implement DataFrame interchange protocol (#46141)

pandas

Python

column.py

def _get_offsets_buffer(self) -> Tuple[PandasBuffer, Any]: if self.dtype[0] == DtypeKind.STRING: # For each string, we need to manually determine the next offset values = self._col.to_numpy() ptr = 0 offsets = np.zeros(shape=(len(values) + 1,), dtype=np.int64) for i, v in enumerate(values): # For missing values (in this case, `np.nan` values) # we don't increment the pointer if isinstance(v, str): b = v.encode(encoding="utf-8") ptr += len(b) offsets[i + 1] = ptr # Convert the offsets to a Pandas "buffer" using # the NumPy array as the backing store buffer = PandasBuffer(offsets) # Assemble the buffer dtype info dtype = ( DtypeKind.INT, 64, ArrowCTypes.INT64, Endianness.NATIVE, ) # note: currently only support native endianness else: raise NoBufferPresent( "This column has a fixed-length dtype so " "it does not have an offsets buffer" ) return buffer, dtype

90140f055892a46f473bd26affab88a7f171e394

https://github.com/pandas-dev/pandas.git

def _get_offsets_buffer(self) -> Tuple[PandasBuffer, Any]: if self.dtype[0] == DtypeKind.STRING: # For each string, we need to manually determine the next offset values = self._col.to_numpy() ptr = 0 offsets = np.zeros(shape=(len(values) + 1,), dtype=np.int64) for i, v in enumerate(values): # For missing values (in this case, `np.nan` values) # we don't increment the pointer if isinstance(v, str): b = v.encode(encoding="utf-8") ptr += len(b) offsets[i + 1] = ptr # Convert the offsets to a Pandas "buffer" using # the NumPy array as the backing store buffer = PandasBuffer(offsets) # Assemble the buffer dtype info dtype = ( DtypeKind.INT, 64, ArrowCTypes.INT64, Endianness.NATIVE, ) # note: currently only support native endianness else: raise NoBufferPresent( "This column has a fixed-length dtype so " "it does not have an offsets buffer" ) return buffer, dtype

_get_offsets_buffer

rllib/evaluate.py

[RLlib] gymnasium support (new `Env.reset()/step()/seed()/render()` APIs). (#28369)

ray

Python

evaluate.py

def append_step(self, obs, action, next_obs, reward, terminated, truncated, info): if self._outfile: if self._save_info: self._current_rollout.append( [obs, action, next_obs, reward, terminated, truncated, info] ) else: self._current_rollout.append( [obs, action, next_obs, reward, terminated, truncated] ) self._total_steps += 1 @eval_app.command()

8e680c483ce326cefc62e44f68ab1a6948b1c3d2

@eval_app.command()

https://github.com/ray-project/ray.git

def append_step(self, obs, action, next_obs, reward, terminated, truncated, info): if self._outfile: if self._save_info: self._current_rollout.append( [obs, action, next_obs, reward, terminated, truncated, info] ) else: self._cur

append_step

django/contrib/gis/gdal/libgdal.py

Refs #33476 -- Reformatted code with Black.

django

Python

libgdal.py

def err_handler(error_class, error_number, message): logger.error("GDAL_ERROR %d: %s", error_number, message) err_handler = CPLErrorHandler(err_handler)

9c19aff7c7561e3a82978a272ecdaad40dda5c00

https://github.com/django/django.git

def err_handler(error_class, error_number, message): logger.error("GDAL_ERROR %d: %s", error_number, messa

err_handler

rllib/agents/pg/pg.py

[CI] Format Python code with Black (#21975) See #21316 and #21311 for the motivation behind these changes.

ray

Python

pg.py

def get_default_policy_class(self, config) -> Type[Policy]: return PGTorchPolicy if config.get("framework") == "torch" else PGTFPolicy

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

https://github.com/ray-project/ray.git

def get_default_policy_class(self, config) -> Type[Policy]: return PGTorchPolicy if config.get("framework") == "torch" else PGTFPolicy

get_default_policy_class

jina/orchestrate/flow/base.py

fix: properly disable graphql endpoint by default (#4471)

jina

Python

base.py

def _get_address_table(self, address_table): address_table.add_row('🔗', 'Protocol: ', f'{self.protocol}') address_table.add_row( '🏠', 'Local access: ', f'[underline]{self.host}:{self.port}[/underline]', ) address_table.add_row( '🔒', 'Private network: ', f'[underline]{self.address_private}:{self.port}[/underline]', ) if self.address_public: address_table.add_row( '🌐', 'Public address: ', f'[underline]{self.address_public}:{self.port}[/underline]', ) if self.protocol == GatewayProtocolType.HTTP: address_table.add_row( '💬', 'Swagger UI: ', f'[underline]http://localhost:{self.port}/docs[/underline]', ) address_table.add_row( '📚', 'Redoc: ', f'[underline]http://localhost:{self.port}/redoc[/underline]', ) if self.args.expose_graphql_endpoint: address_table.add_row( '💬', 'GraphQL UI: ', f'[underline][cyan]http://localhost:{self.port}/graphql[/underline][/cyan]', ) return address_table

9bdefb0a693c4556e0902ef913f4426e3ef5ff65

https://github.com/jina-ai/jina.git

def _get_address_table(self, address_table): address_table.add_row('🔗', 'Protocol: ', f'{self.protocol}') address_table.add_row( '🏠', 'Local access: ', f'[underline]{self.host}:{self.port}[/underline]', ) address_table.add_row( '🔒', 'Private network: ', f'[underline]{self.address_private}:{self.port}[/underline]', ) if self.address_public: address_table.add_row( '🌐', 'Public address: ', f'[underline]{self.address_public}:{self.port}[/underline]', ) if self.protocol == GatewayProtocolType.HTTP: address_table.add_row( '💬', 'Swagger UI: ', f'[underline]http://localhost:{self.port}/docs[/underline]', ) address_table.add_row( '📚', 'Redoc: ', f'[underline]http://localhost:{self.port}/redoc[/underline]', ) if self.args.expose_graphql_e

_get_address_table

test/test_prototype_builtin_datasets.py

refactor prototype datasets tests (#5136) * refactor prototype datasets tests * skip tests with insufficient third party dependencies

vision

Python

test_prototype_builtin_datasets.py

def test_sample(self, dataset_mock, config): dataset, _ = dataset_mock.load(config) try: sample = next(iter(dataset)) except Exception as error: raise AssertionError("Drawing a sample raised the error above.") from error if not isinstance(sample, dict): raise AssertionError(f"Samples should be dictionaries, but got {type(sample)} instead.") if not sample: raise AssertionError("Sample dictionary is empty.")

3e79d149a16e9fc9b9b6747609615cf8f8607346

https://github.com/pytorch/vision.git

def test_sample(self, dataset_mock, config): dataset, _ = dataset_mock.load(config) try: sample = next(iter(dataset)) except Exception as error: raise AssertionError("Drawing a sample raised the error above.") from error if not isinstance(sample, dict):

test_sample

tests/components/http/test_init.py

Startup with an emergency self signed cert if the ssl certificate cannot be loaded (#66707)

core

Python

test_init.py

def _setup_broken_ssl_pem_files(tmpdir): test_dir = tmpdir.mkdir("test_broken_ssl") cert_path = pathlib.Path(test_dir) / "cert.pem" cert_path.write_text("garbage") key_path = pathlib.Path(test_dir) / "key.pem" key_path.write_text("garbage") return cert_path, key_path

3bf2be1765f7a33fbce06cbabeb2e2115f2f07c7

https://github.com/home-assistant/core.git

def _setup_broken_ssl_pem_files(tmpdir): test_dir = tmpdir.mkdir("test_broken_ssl") cert_path = pathlib.Path(test_dir) / "cert.pem" cert_path.write_text("garbage") key_path = pathlib.Path(test_dir) / "key.pem" key_path.write_text("garbage") return cert_path, key_path

_setup_broken_ssl_pem_files

src/prefect/client/orion.py

Improve client stability (#7090)

prefect

Python

orion.py

async def api_healthcheck(self) -> Optional[Exception]: try: await self._client.get("/health") return None except Exception as exc: return exc

852644aa77ce2a6377e070ed0182859b0fdd0b98

https://github.com/PrefectHQ/prefect.git

async def api_healthcheck(self) -> Optional[Exception]: try: await self._client.get("/health") return None except Exception as exc: return exc

api_healthcheck

ludwig/backend/ray.py

Respect the resource requests in RayPredictor (#2359) * fix ordering to respect the passed down resources * clean up

ludwig

Python

ray.py

def get_resources_per_worker(self) -> Tuple[int, int]: trainer_kwargs = self.get_trainer_kwargs() resources_per_worker = trainer_kwargs.get("resources_per_worker", {}) num_gpus = resources_per_worker.get("GPU", 0) num_cpus = resources_per_worker.get("CPU", (1 if num_gpus == 0 else 0)) return num_cpus, num_gpus

15961c07d8a0d7fc25c5b7395b11f191eab840bd

https://github.com/ludwig-ai/ludwig.git

def get_resources_per_worker(self) -> Tuple[int, int]: trainer_kwargs = self.get_trainer_kwargs() resources_per_worker = trainer_kwargs.get("resources_per_worker", {}) num_gpus = resources_per_worker.get("GPU", 0) num_cpus = resources_per_worker.get("CPU", (1 if num_g

get_resources_per_worker

bots/stocks/technical_analysis/aroon.py

Refactor Bot (#1326) * First commit * Renamed to bot * change from discordbot to bots imports * refractor cmds + autocomplete helpers + updated dps plots/candle + quote cmd * isort fix * Update helpers.py * Added first refactor * Added commands and refactor * Added description handler * Through grains * ta + image border helper * Added indices * merged * Finished econ * options refactor * dark_pool_shorts, candle, quote done * pagination/dropdown logic to helpers + fixes * dd/disc/insider done + add image logic to helpers * linting * removed plt style * Almost done with Gov * fixing conflicts * Finished gov (excpect ones mentioned) * screener done * Test bug refactor * Store changes to switch * Finished refactor * Fixed error * Delete ::40 * Fixed issue * Fixed black * finished * fixes Co-authored-by: teh_coderer <me@tehcoderer.com> Co-authored-by: didierlopes.eth <dro.lopes@campus.fct.unl.pt>

OpenBBTerminal

Python

aroon.py

def aroon_command(ticker="", length="25", scalar="100", start="", end=""): # Debug if cfg.DEBUG: logger.debug( "ta-aroon %s %s %s %s %s", ticker, length, scalar, start, end, ) # Check for argument if ticker == "": raise Exception("Stock ticker is required") if start == "": start = datetime.now() - timedelta(days=365) else: start = datetime.strptime(start, cfg.DATE_FORMAT) if end == "": end = datetime.now() else: end = datetime.strptime(end, cfg.DATE_FORMAT) if not length.lstrip("-").isnumeric(): raise Exception("Number has to be an integer") length = int(length) if not scalar.lstrip("-").isnumeric(): raise Exception("Number has to be an integer") scalar = float(scalar) ticker = ticker.upper() df_stock = helpers.load(ticker, start) if df_stock.empty: raise Exception("Stock ticker is invalid") # Retrieve Data df_stock = df_stock.loc[(df_stock.index >= start) & (df_stock.index < end)] df_ta = trend_indicators_model.aroon( df_stock["High"], df_stock["Low"], length, scalar ) # Output Data aadown = df_ta.columns[0].replace("_", " ") aaup = df_ta.columns[1].replace("_", " ") aaosc = df_ta.columns[2].replace("_", " ") fig = make_subplots( rows=3, cols=1, shared_xaxes=True, vertical_spacing=0.07, row_width=[0.2, 0.2, 0.2], ) fig.add_trace( go.Scatter( name=ticker, x=df_stock.index, y=df_stock["Adj Close"].values, line=dict(color="#fdc708", width=2), opacity=1, showlegend=False, ), row=1, col=1, ) fig.add_trace( go.Scatter( name=f"Aroon DOWN ({aadown})", x=df_ta.index, y=df_ta.iloc[:, 0].values, opacity=1, ), row=2, col=1, ) fig.add_trace( go.Scatter( name=f"Aroon UP ({aaup})", x=df_ta.index, y=df_ta.iloc[:, 1].values, opacity=1, ), row=2, col=1, ) fig.add_trace( go.Scatter( name=f"Aroon OSC ({aaosc})", x=df_ta.index, y=df_ta.iloc[:, 2].values, opacity=1, ), row=3, col=1, ) fig.add_hline( y=50, fillcolor="grey", opacity=1, layer="below", line_width=3, line=dict(color="grey", dash="dash"), row=2, col=1, ) fig.update_layout( margin=dict(l=0, r=20, t=30, b=20), template=cfg.PLT_TA_STYLE_TEMPLATE, colorway=cfg.PLT_TA_COLORWAY, title=f"Aroon on {ticker}", title_x=0.5, yaxis_title="Stock Price ($)", yaxis=dict( fixedrange=False, ), xaxis=dict( rangeslider=dict(visible=False), type="date", ), dragmode="pan", legend=dict(yanchor="top", y=0.99, xanchor="left", x=0.01), ) config = dict({"scrollZoom": True}) imagefile = "ta_aroon.png" # Check if interactive settings are enabled plt_link = "" if cfg.INTERACTIVE: html_ran = helpers.uuid_get() fig.write_html(f"in/aroon_{html_ran}.html", config=config) plt_link = f"[Interactive]({cfg.INTERACTIVE_URL}/aroon_{html_ran}.html)" fig.update_layout( width=800, height=500, ) imagefile = helpers.image_border(imagefile, fig=fig) return { "title": f"Stocks: Aroon-Indicator {ticker}", "description": plt_link, "imagefile": imagefile, }

fae93c67adc9015c1466712f9c8ffa35a8b70872

https://github.com/OpenBB-finance/OpenBBTerminal.git

def aroon_command(ticker="", length="25", scalar="100", start="", end=""): # Debug if cfg.DEBUG: logger.debug( "ta-aroon %s %s %s %s %s", ticker, length, scalar, start, end, ) # Check for argument if ticker == "": raise Exception("Stock ticker is required") if start == "": start = datetime.now() - timedelta(days=365) else: start = datetime.strptime(start, cfg.DATE_FORMAT) if end == "": end = datetime.now() else: end = datetime.strptime(end, cfg.DATE_FORMAT) if not length.lstrip("-").isnumeric(): raise Excep

aroon_command

python/ray/serve/config.py

[Serve] Add test for controller managing Java Replica (#22628)

ray

Python

config.py

def to_proto_bytes(self): data = self.dict() if data.get("user_config"): data["user_config"] = pickle.dumps(data["user_config"]) if data.get("autoscaling_config"): data["autoscaling_config"] = AutoscalingConfigProto( **data["autoscaling_config"] ) return DeploymentConfigProto(**data).SerializeToString()

0bab8dbfe0b5a58a06dd226950bdd70b0b186655

https://github.com/ray-project/ray.git

def to_proto_bytes(self): data = self.dict() if data.get("user_config"): data["user_conf

to_proto_bytes

homeassistant/components/plex/media_player.py

Use new media player enums [p] (#78058)

core

Python

media_player.py

def session(self, session): self._session = session if session: self.session_device = self.session.player self.update_state(self.session.state) else: self._attr_state = MediaPlayerState.IDLE

52b5e1779f1ed6e5005dc0bdff4137040d7216fb

https://github.com/home-assistant/core.git

def session(self, session): self._session = session if session: self.session_device = self.session

session

python3.10.4/Lib/difflib.py

add python 3.10.4 for windows

XX-Net

Python

difflib.py

def get_grouped_opcodes(self, n=3): codes = self.get_opcodes() if not codes: codes = [("equal", 0, 1, 0, 1)] # Fixup leading and trailing groups if they show no changes. if codes[0][0] == 'equal': tag, i1, i2, j1, j2 = codes[0] codes[0] = tag, max(i1, i2-n), i2, max(j1, j2-n), j2 if codes[-1][0] == 'equal': tag, i1, i2, j1, j2 = codes[-1] codes[-1] = tag, i1, min(i2, i1+n), j1, min(j2, j1+n) nn = n + n group = [] for tag, i1, i2, j1, j2 in codes: # End the current group and start a new one whenever # there is a large range with no changes. if tag == 'equal' and i2-i1 > nn: group.append((tag, i1, min(i2, i1+n), j1, min(j2, j1+n))) yield group group = [] i1, j1 = max(i1, i2-n), max(j1, j2-n) group.append((tag, i1, i2, j1 ,j2)) if group and not (len(group)==1 and group[0][0] == 'equal'): yield group

8198943edd73a363c266633e1aa5b2a9e9c9f526

https://github.com/XX-net/XX-Net.git

def get_grouped_opcodes(self, n=3): codes = self.get_opcodes() if not codes: codes = [("equal", 0, 1, 0, 1)] # Fixup leading and trailing groups if they show no changes. if codes[0][0] == 'equal': tag, i1, i2, j1, j2 = codes[0] codes[0] = tag, max(i1, i2-n), i2, max(j1, j2-n), j2 if codes[-1][0] == 'equal': tag, i1, i2, j1, j2 = codes[-1] codes[-1] = tag, i1, min(i2, i1+n), j1, min(j2, j1+n) nn = n + n group = [] for tag, i1, i2, j1, j2 in codes: # End the current group and start a new one whenever # there is a large range with no changes. if tag == 'equal' and i2-i1 > nn: group.append((tag, i1, min(i2, i1+n), j1, min(j2, j1+n))) yield group group = [] i1, j1 = max(i1, i2-n), max(j1, j2-n)

get_grouped_opcodes

wagtail/admin/forms/pages.py

Drop support for Django <3.2

wagtail

Python

pages.py

def is_valid(self): comments = self.formsets.get('comments') # Remove the comments formset if the management form is invalid if comments and not comments.management_form.is_valid(): del self.formsets['comments'] return super().is_valid()

d6d43338efdeb85395918d106a1cb3f187ab6fa7

https://github.com/wagtail/wagtail.git

def is_valid(self): comments = self.formsets.get('comments') # Remove the comments formset if the management form is invalid if comments and not comments.management_form.is_valid

is_valid

tests/unit/hubble/test_hubio.py

feat(hub): add --no-cache option to "jina hub push" cli (#4594)

jina

Python

test_hubio.py

def test_push(mocker, monkeypatch, path, mode, tmpdir, force, tag, no_cache): mock = mocker.Mock()

fab9f0736c8d99558d93020cb3f27108627218f1

https://github.com/jina-ai/jina.git

def test_push(mocker, monkeypatch, path, mode, tmpdir, force, tag, no_cache): mock = mocker.Mock()

test_push

label_studio/data_manager/managers.py

fix: DEV-2214: Use contain instead of icontain in annotation result (#2308) * fix: Annotation result performance with contain * Back * Works! * Some * Try to fix completed at performance * Fix tests * Add merge migration

label-studio

Python

managers.py

def apply_filters(queryset, filters, project): if not filters: return queryset # convert conjunction to orm statement filter_expressions = [] for _filter in filters.items: # we can also have annotations filters if not _filter.filter.startswith("filter:tasks:") or _filter.value is None: continue # django orm loop expression attached to column name preprocess_field_name = load_func(settings.PREPROCESS_FIELD_NAME) field_name, _ = preprocess_field_name(_filter.filter, project.only_undefined_field) # filter preprocessing, value type conversion, etc.. preprocess_filter = load_func(settings.DATA_MANAGER_PREPROCESS_FILTER) _filter = preprocess_filter(_filter, field_name) # custom expressions for enterprise custom_filter_expressions = load_func(settings.DATA_MANAGER_CUSTOM_FILTER_EXPRESSIONS) filter_expression = custom_filter_expressions(_filter, field_name) if filter_expression: filter_expressions.append(filter_expression) continue # annotators if field_name == 'annotators' and _filter.operator == Operator.CONTAINS: filter_expressions.append(Q(annotations__completed_by=int(_filter.value))) continue elif field_name == 'annotators' and _filter.operator == Operator.NOT_CONTAINS: filter_expressions.append(~Q(annotations__completed_by=int(_filter.value))) continue elif field_name == 'annotators' and _filter.operator == Operator.EMPTY: value = cast_bool_from_str(_filter.value) filter_expressions.append(Q(annotations__completed_by__isnull=value)) continue # annotations results & predictions results if field_name in ['annotations_results', 'predictions_results']: result = add_result_filter(field_name, _filter, filter_expressions, project) if result == 'exit': return queryset.none() elif result == 'continue': continue # annotation ids if field_name == 'annotations_ids': field_name = 'annotations__id' if 'contains' in _filter.operator: # convert string like "1 2,3" => [1,2,3] _filter.value = [int(value) for value in re.split(',|;| ', _filter.value) if value and value.isdigit()] _filter.operator = 'in_list' if _filter.operator == 'contains' else 'not_in_list' elif 'equal' in _filter.operator: if not _filter.value.isdigit(): _filter.value = 0 # annotators if field_name == 'annotators' and _filter.operator == Operator.CONTAINS: filter_expressions.append(Q(annotations__completed_by=int(_filter.value))) continue elif field_name == 'annotators' and _filter.operator == Operator.NOT_CONTAINS: filter_expressions.append(~Q(annotations__completed_by=int(_filter.value))) continue elif field_name == 'annotators' and _filter.operator == Operator.EMPTY: value = cast_bool_from_str(_filter.value) filter_expressions.append(Q(annotations__completed_by__isnull=value)) continue # predictions model versions if field_name == 'predictions_model_versions' and _filter.operator == Operator.CONTAINS: q = Q() for value in _filter.value: q |= Q(predictions__model_version__contains=value) filter_expressions.append(q) continue elif field_name == 'predictions_model_versions' and _filter.operator == Operator.NOT_CONTAINS: q = Q() for value in _filter.value: q &= ~Q(predictions__model_version__contains=value) filter_expressions.append(q) continue elif field_name == 'predictions_model_versions' and _filter.operator == Operator.EMPTY: value = cast_bool_from_str(_filter.value) filter_expressions.append(Q(predictions__model_version__isnull=value)) continue # use other name because of model names conflict if field_name == 'file_upload': field_name = 'file_upload_field' # annotate with cast to number if need if _filter.type == 'Number' and field_name.startswith('data__'): json_field = field_name.replace('data__', '') queryset = queryset.annotate(**{ f'filter_{json_field.replace("$undefined$", "undefined")}': Cast(KeyTextTransform(json_field, 'data'), output_field=FloatField()) }) clean_field_name = f'filter_{json_field.replace("$undefined$", "undefined")}' else: clean_field_name = field_name # special case: predictions, annotations, cancelled --- for them 0 is equal to is_empty=True if clean_field_name in ('total_predictions', 'total_annotations', 'cancelled_annotations') and \ _filter.operator == 'empty': _filter.operator = 'equal' if cast_bool_from_str(_filter.value) else 'not_equal' _filter.value = 0 # get type of annotated field value_type = 'str' if queryset.exists(): value_type = type(queryset.values_list(field_name, flat=True)[0]).__name__ if (value_type == 'list' or value_type == 'tuple') and 'equal' in _filter.operator: raise Exception('Not supported filter type') # special case: for strings empty is "" or null=True if _filter.type in ('String', 'Unknown') and _filter.operator == 'empty': value = cast_bool_from_str(_filter.value) if value: # empty = true q = Q( Q(**{field_name: None}) | Q(**{field_name+'__isnull': True}) ) if value_type == 'str': q |= Q(**{field_name: ''}) if value_type == 'list': q = Q(**{field_name: [None]}) else: # empty = false q = Q( ~Q(**{field_name: None}) & ~Q(**{field_name+'__isnull': True}) ) if value_type == 'str': q &= ~Q(**{field_name: ''}) if value_type == 'list': q = ~Q(**{field_name: [None]}) filter_expressions.append(q) continue # regex pattern check elif _filter.operator == 'regex': try: re.compile(pattern=str(_filter.value)) except Exception as e: logger.info('Incorrect regex for filter: %s: %s', _filter.value, str(e)) return queryset.none() # append operator field_name = f"{clean_field_name}{operators.get(_filter.operator, '')}" # in if _filter.operator == "in": cast_value(_filter) filter_expressions.append( Q( **{ f"{field_name}__gte": _filter.value.min, f"{field_name}__lte": _filter.value.max, } ), ) # not in elif _filter.operator == "not_in": cast_value(_filter) filter_expressions.append( ~Q( **{ f"{field_name}__gte": _filter.value.min, f"{field_name}__lte": _filter.value.max, } ), ) # in list elif _filter.operator == "in_list": filter_expressions.append( Q(**{f"{field_name}__in": _filter.value}), ) # not in list elif _filter.operator == "not_in_list": filter_expressions.append( ~Q(**{f"{field_name}__in": _filter.value}), ) # empty elif _filter.operator == 'empty': if cast_bool_from_str(_filter.value): filter_expressions.append(Q(**{field_name: True})) else: filter_expressions.append(~Q(**{field_name: True})) # starting from not_ elif _filter.operator.startswith("not_"): cast_value(_filter) filter_expressions.append(~Q(**{field_name: _filter.value})) # all others else: cast_value(_filter) filter_expressions.append(Q(**{field_name: _filter.value})) logger.debug(f'Apply filter: {filter_expressions}') if filters.conjunction == ConjunctionEnum.OR: result_filter = Q() for filter_expression in filter_expressions: result_filter.add(filter_expression, Q.OR) queryset = queryset.filter(result_filter) else: for filter_expression in filter_expressions: queryset = queryset.filter(filter_expression) return queryset

5e054ce3ab751f3445d2fc1b2923bb604048c40b

https://github.com/heartexlabs/label-studio.git

def apply_filters(queryset, filters, project): if not filters: return queryset # convert conjunction to orm statement filter_expressions = [] for _filter in filters.items: # we can also have annotations filters if not _filter.filter.startswith("filter:tasks:") or _filter.value is None: continue # django orm loop expression attached to column name preprocess_field_name = load_func(settings.PREPROCESS_FIELD_NAME) field_name, _ = preprocess_field_name(_filter.filter, project.only_undefined_field) # filter preprocessing, value type conversion, etc.. preprocess_filter = load_func(settings.DATA_MANAGER_PREPROCESS_FILTER) _filter = preprocess_filter(_filter, field_name) # custom expressions for enterprise custom_filter_expressions = load_func(settings.DATA_MANAGER_CUSTOM_FILTER_EXPRESSIONS) filter_expression = custom_filter_expressions(_filter, field_name) if filter_expression: filter_expressions.append(filter_expression) continue # annotators if field_name == 'annotators' and _filter.operator == Operator.CONTAINS: filter_expressions.append(Q(annotations__completed_by=int(_filter.value))) continue elif field_name == 'annotators' and _filter.operator == Operator.NOT_CONTAINS: filter_expressions.append(~Q(annotations__completed_by=int(_filter.value))) continue elif field_name == 'annotators' and _filter.operator == Operator.EMPTY: value = cast_bool_from_str(_filter.value) filter_expressions.append(Q(annotations__completed_by__isnull=value)) continue # annotations results & predictions results if field_name in ['annotations_results', 'predictions_results']: result = add_result_filter(field_name, _filter, filter_expressions, project) if result == 'exit': return queryset.none() elif result == 'continue': continue # annotation ids if field_name == 'annotations_ids': field_name = 'annotations__id' if 'contains' in _filter.operator: # convert string like "1 2,3" => [1,2,3] _filter.value = [int(value) for value in re.split(',|;| ', _filter.value) if value and value.isdigit()] _filter.operator = 'in_list' if _filter.operator == 'contains' else 'not_in_list' elif 'equal' in _filter.operator: if not _filter.value.isdigit(): _filter.value = 0 # annotators if field_name == 'annotators' and _filter.operator == Operator.CONTAINS: filter_expressions.append(Q(annotations__completed_by=int(_filter.value))) continue elif field_name == 'annotators' and _filter.operator == Operator.NOT_CONTAINS: filter_expressions.append(~Q(annotations__completed_by=int(_filter.value))) continue elif field_name == 'annotators' and _filter.operator == Operator.EMPTY: value = cast_bool_from_str(_filter.value) filter_expressions.append(Q(annotations__completed_by__isnull=value)) continue # predictions model versions if field_name == 'predictions_model_versions' and _filter.operator == Operator.CONTAINS: q = Q() for value in _filter.value: q |= Q(predictions__model_version__contains=value) filter_expressions.append(q) continue elif field_name == 'predictions_model_versions' and _filter.operator == Operator.NOT_CONTAINS: q = Q() for value in _filter.value: q &= ~Q(predictions__model_version__contains=value) filter_expressions.append(q) continue elif field_name == 'predictions_model_versions' and _filter.operator == Operator.EMPTY: value = cast_bool_from_str(_filter.value) filter_expressions.append(Q(predictions__model_version__isnull=value)) continue # use other name because of model names conflict if field_name == 'file_upload': field_name = 'file_upload_field' # annotate with cast to number if need if _filter.type == 'Number' and field_name.startswith('data__'): json_field = field_name.replace('data__', '') queryset = queryset.annotate(**{ f'filter_{json_field.replace("$undefined$", "undefined")}': Cast(KeyTextTransform(json_field, 'data'), output_field=FloatField()) }) clean_field_name = f'filter_{json_field.replace("$undefined$", "undefined")}' else: clean_field_name = field_name # special case: predictions, annotations, cancelled --- for them 0 is equal to is_empty=True if clean_field_name in ('total_predictions', 'total_annotations', 'cancelled_annotations') and \ _filter.operator == 'empty': _filter.operator = 'equal' if cast_bool_from_str(_filter.value) else 'not_equal' _filter.value = 0 # get type of annotated field value_type = 'str' if queryset.exists(): value_type = type(queryset.values_list(field_name, flat=True)[0]).__name__ if (value_type == 'list' or value_type == 'tuple') and 'equal' in _filter.operator: raise Exception('Not supported filter type') # special case: for strings empty is "" or null=True if _filter.type in ('String', 'Unknown') and _filter.operator == 'empty': value = cast_bool_from_str(_filter.value) if value: # empty = true q = Q( Q(**{field_name: None}) | Q(**{field_name+'__isnull': True}) ) if value_type == 'str': q |= Q(**{field_name: ''}) if value_type == 'list': q = Q(**{field_name: [None]}) else: # empty = false q = Q( ~Q(**{field_name: None}) & ~Q(**{field_name+'__isnull': True}) ) if value_type == 'str': q &= ~Q(**{field_name: ''}) if value_type == 'list': q = ~Q(**{field_name: [None]}) filter_expressions.append(q) continue # regex pattern check elif _filter.operator == 'regex': try: re.compile(pattern=str(_filter.value)) except Exception as e: logger.info('Incorrect regex for filter: %s: %s', _filter.value, str(e)) return queryset.none() # append operator field_name = f"{clean_field_name}{operators.get(_filter.operator, '')}" # in if _filter.operator == "in

apply_filters

homeassistant/components/zha/light.py

Improve type hints in zha light (#75947)

core

Python

light.py

async def async_update(self) -> None: if self._transitioning: self.debug("skipping async_update while transitioning") return await self.async_get_state()

9af64b1c3b1a712d24fc7b86ed2cc5e1fa613f26

https://github.com/home-assistant/core.git

async def async_update(self) -> None: if self._transitioning: self.debug("skipping asy

async_update

tests/openbb_terminal/stocks/discovery/test_finnhub_view.py

Here we merge all API Refactor related branches (#2236) * Update api.py * Updated forex menu * refactor ycrv command * refactor ycrv command black * refactor ecocal command * Minh changes * Adding space to test pushing * title fix ecocal df * get economic calendar annotation * fix investingcom tests * refactor index command * refactor overview command * give defaults to wsj view function args * rename date args investincom * refacto bigmac command * fix ecocal typo * refactor rtps command * alphavantage gdp * alphavantage gdp per capita * alphavantage cpi * alphavantage tyld * alphavantage inf * refactor macro command * refactor macro command w helpers * refactor treasury command * fix macro on terminal * treasury labels * refactor maturities * update treasury maturities doc strings * refactor get economic calendar finhub * refactor map command api * display map filter choices * route economy api to performance map * route economy api to performance map * display group choices on valuation command * refactor performance and valuation commands * refactor spectrum model and view * add choices to spectrum controller * delete image after view * fix model tests finviz * fix finciz view tests * refactor futures * fix some tests * fix more tests * fix controller test * refactor fred series notes * update fred notes docstring * refacto fred series ids * fix pred and qa when empty datasets * refactor fred * uncomment stuff * refacto get series data * fix some tests * set defaults on args * refactor fred yield curve * black * fix spell and remove ecocal names * fix linting * linting * pylint fix * change dangerous defaults * Working through crypto fixes (#2256) * Working through crypto fixes * Continued adding crypto stuff * Added crypto overview * Added test fixes * Added fixtures * Fixed tests * Fixed charting issue * Removed broken APIs * Final adjustments * Added test fixes * map get groups and get ycrv countries into old api * exposed econdb helper funcs * remove helpers * refactor search indices * linting * refactor arg currency * pylint from currency * Started switching crpyto ascending to ascend * Merging * Portfolio model arguements, params, and docstring * Refactored for etf commands (#2292) * Refactored for etf commands * Fixed tests * Added load command * Fixed menu * Portfolio logic fixes * Added econometrics (#2260) * Added econometrics * Fixed tests * Simplified API * Added test fixes * Added test csv * Allowed examples to be loaded * Fund refactor (#2291) * Fund refactor * Changed fund_name and fund to name * Changed ascending to ascend * Stock menu refactoring for easier API usage (#2194) * Stocks refactoring for easier API usage * Linting * Refactor newly added features * Linting * Fixing tests * Refactor common files used by stocks menu * Fixing flake8 * Fix linting and tests * Linting * Fix flake8 * refactor insider_data * refactor mentions * refactor watchlist * refactor sentiment * refactor sentiment * fix yahoofinance tests * refactor load and candle * refactor get_news and display_news * refactor stocks.ins.act * candle default matplotlib * fix yahoofinance_view tests * fix ark model tests * fix ark view tests * fix business insider model * fix business insider view * refactor csimarket model * fix tests csi market model * update dd controller * fix get suppliers tests * fix dd controller tests * fix finhub tests * fix finviz tests * fix fmp tests * fix marketwatch tests * corrected argument keywords in test_bt_model * corrected argument keywords in test_bt_view * refactor fa controller * refactor marketwatch view * refactor gov controller * fix tests fa av * fix tests elect * fix dcf tests * fix polygon tests * fix fmp tests * fix quiverquant tests * fix yahoofinance fa tests * fix more fa tests * fix insider tests * fix more tests * fix more tests * fix options tests * fix stock gov tests * fix tests test_ba_controller * fix tests for test_finviz_compare_model.py * fixed 2 tests * fixed tests * fixed tests * fixed tests * fixed tests * fixed tests * fixed tests * fixed tests * fixed tests * fixed tests * fixed tests * fix final tests * fixed tests * fixed tests * Fix tests * black * forgot to black tests * fixed tests * fixed tests * fixed tests * fixed tests * flakefix * Tests + code : Stocks / Discovery * fix tests * added recorder * fixed tests * fixed tests * black * black * remove unused imports * refactor display raw * sia dicts fix * pylint * linting * remove dangerous default * fix tests * fix beta model test * black * skip screener qa test * change sector path to sectors * update tests readme * fix metric defaults * black * substitute lost ticker * defaults cpic * another round on sia * refactor cramer * reduce default tweets on sentiment * refactor yf hist, corr, volume * arkorders default * refactor income, balance, cashflow * refacto scorr, screener, getfinnhub * refactor stockgrid * ibkr refactor * another round on stockgrid * add dividens end point * refactor discovery endpoints * update docstrings with similar input * refactor messages * refactor ba * refactor regioons * refactor twitter sentiment * refactor hist * refactor regions * give default to timeframe * refactor bunch of defaults and arg names * remove leftover imports * refactor vwap * let tests run * fix tests * fix stock tests * fix stockanalysis tests * flake * MYPY * Made important changes * added fixes * Fixed big issue * Added fixes to tests * fix qa tests * fix tests * fix 1 more test * last stocks failing * fix crypto test Co-authored-by: Chavithra PARANA <chavithra@gmail.com> Co-authored-by: montezdesousa <montezdesousa@gmail.com> Co-authored-by: hjoaquim <h.joaquim@campus.fct.unl.pt> Co-authored-by: montezdesousa <79287829+montezdesousa@users.noreply.github.com> Co-authored-by: colin99d <colin99delahunty@gmail.com> * fix portfolio tests * change period to window * update ca docstrings * refactor get_similar_companies func * Fixed * Update CI * Update CI 2 * Update CI 3 * Update dependencies Co-authored-by: colin99d <colin99delahunty@gmail.com> Co-authored-by: Colin Delahunty <72827203+colin99d@users.noreply.github.com> Co-authored-by: montezdesousa <montezdesousa@gmail.com> Co-authored-by: James Simmons <simmonsj330@gmail.com> Co-authored-by: Theodore Aptekarev <aptekarev@gmail.com> Co-authored-by: minhhoang1023 <40023817+minhhoang1023@users.noreply.github.com> Co-authored-by: jose-donato <43375532+jose-donato@users.noreply.github.com> Co-authored-by: montezdesousa <79287829+montezdesousa@users.noreply.github.com> Co-authored-by: northern-64bit <75195383+northern-64bit@users.noreply.github.com> Co-authored-by: hjoaquim <h.joaquim@campus.fct.unl.pt>

OpenBBTerminal

Python

test_finnhub_view.py

def test_past_ipo_empty_df(mocker): mocker.patch( "openbb_terminal.stocks.discovery.finnhub_view.finnhub_model.get_ipo_calendar", return_value=pd.DataFrame(), ) finnhub_view.past_ipo( num_days_behind=2, start_date="2021-12-01", limit=20, export="" ) @pytest.mark.vcr(record_mode="none") @pytest.mark.record_stdout

9e1a58e2dbedec4e4a9f9c2e32ddf091776c606b

@pytest.mark.vcr(record_mode="none") @pytest.mark.record_stdout

https://github.com/OpenBB-finance/OpenBBTerminal.git

def test_past_ipo_empty_df(mocker): mocker.patch(

test_past_ipo_empty_df

sklearn/semi_supervised/tests/test_self_training.py

MNT Replace if_delegate_has_method with available_if in ensemble and semi_supervised (#20545) Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

scikit-learn

Python

test_self_training.py

def test_base_estimator_meta_estimator(): # Check that a meta-estimator relying on an estimator implementing # `predict_proba` will work even if it does expose this method before being # fitted. # Non-regression test for: # https://github.com/scikit-learn/scikit-learn/issues/19119 base_estimator = StackingClassifier( estimators=[ ("svc_1", SVC(probability=True)), ("svc_2", SVC(probability=True)), ], final_estimator=SVC(probability=True), cv=2, ) assert hasattr(base_estimator, "predict_proba") clf = SelfTrainingClassifier(base_estimator=base_estimator) clf.fit(X_train, y_train_missing_labels) clf.predict_proba(X_test) base_estimator = StackingClassifier( estimators=[ ("svc_1", SVC(probability=False)), ("svc_2", SVC(probability=False)), ], final_estimator=SVC(probability=False), cv=2, ) assert not hasattr(base_estimator, "predict_proba") clf = SelfTrainingClassifier(base_estimator=base_estimator) with pytest.raises(AttributeError): clf.fit(X_train, y_train_missing_labels)

a794c58692a1f3e7a85a42d8c7f7ddd5fcf18baa

https://github.com/scikit-learn/scikit-learn.git

def test_base_estimator_meta_estimator(): # Check that a meta-estimator relying on an estimator implementing # `predict_proba` will work even if it does expose this method before being # fitted. # Non-regression test for: # https://github.com/scikit-learn/scikit-learn/issues/19119 base_estimator = StackingClassifier( estimators=[ ("svc_1", SVC(probability=True)), ("svc_2", SVC(probability=True)), ], final_estimator=SVC(probability=True), cv=2, ) assert hasat

test_base_estimator_meta_estimator

rllib/tests/test_io.py

[RLlib] QMIX better defaults + added to CI learning tests (#21332)

ray

Python

test_io.py

def test_agent_input_eval_sim(self): for fw in framework_iterator(): self.write_outputs(self.test_dir, fw) agent = PGTrainer( env="CartPole-v0", config={ "input": self.test_dir + fw, "input_evaluation": ["simulation"], "framework": fw, }) for _ in range(50): result = agent.train() if not np.isnan(result["episode_reward_mean"]): return # simulation ok time.sleep(0.1) assert False, "did not see any simulation results"

abd3bef63b486fe3e43c1608d93205a702880414

https://github.com/ray-project/ray.git

def test_agent_input_eval_sim(self): for fw in framework_iterator(): self.write_outputs(self.test_dir, fw) agent = PGTrainer( env="CartPole-v0", config={ "input": self.test_dir + fw

test_agent_input_eval_sim

ppdet/modeling/heads/s2anet_head.py

refactor s2anet (#6604) * refactor s2anet to support batch_size > 1 * fix problem of inference * support batch_size > 1 for training * fix empty results * fix dota eval * fix configs of s2anet_head * modify s2anet_spine_1x to 73 mAP

PaddleDetection

Python

s2anet_head.py

def get_bboxes_single(self, cls_score_list, bbox_pred_list): mlvl_bboxes = [] mlvl_scores = [] for cls_score, bbox_pred in zip(cls_score_list, bbox_pred_list): if self.use_sigmoid_cls: scores = F.sigmoid(cls_score) else: scores = F.softmax(cls_score, axis=-1) if scores.shape[0] > self.nms_pre: # Get maximum scores for foreground classes. if self.use_sigmoid_cls: max_scores = paddle.max(scores, axis=1) else: max_scores = paddle.max(scores[:, :-1], axis=1) topk_val, topk_inds = paddle.topk(max_scores, self.nms_pre) bbox_pred = paddle.gather(bbox_pred, topk_inds) scores = paddle.gather(scores, topk_inds) mlvl_bboxes.append(bbox_pred) mlvl_scores.append(scores) mlvl_bboxes = paddle.concat(mlvl_bboxes) mlvl_scores = paddle.concat(mlvl_scores) mlvl_polys = rbox2poly(mlvl_bboxes).unsqueeze(0) mlvl_scores = paddle.transpose(mlvl_scores, [1, 0]).unsqueeze(0) bbox, bbox_num, _ = self.nms(mlvl_polys, mlvl_scores) if bbox.shape[0] <= 0: bbox = self.fake_bbox bbox_num = self.fake_bbox_num return bbox, bbox_num

b4727677751081b257c6fa23c3c124ab9e5a32a1

https://github.com/PaddlePaddle/PaddleDetection.git

def get_bboxes_single(self, cls_score_list, bbox_pred_list): mlvl_bboxes = [] mlvl_scores = [] for cls_score, bbox_pred in zip(cls_score_list, bbox_pred_list): if self.use_sigmoid_cls: scores = F.sigmoid(cls_score) else: scores = F.softmax(cls_score, axis=-1) if scores.shape[0] > self.nms_pre: # Get maximum scores for foreground classes. if self.use_sigmoid_cls: max_scores = paddle.max(scores, axis=1) else: max_scores = paddle.max(scores[:, :-1], axis=1) topk_val, topk_inds = paddle.topk(max_scores, self.nms_pre) bbox_pred = paddle.gather(bbox_pred, topk_inds) scores = paddle.gather(scores, topk_inds) mlvl_bboxes.append(bbox_pred) mlvl_scores.append(scores) mlvl_bboxes = paddle.concat(mlvl_bboxes) mlvl_scores = paddle.concat(ml

get_bboxes_single

tests/components/onvif/__init__.py

Add diagnostics to ONVIF (#69708)

core

Python

__init__.py

def setup_mock_device(mock_device): mock_device.async_setup = AsyncMock(return_value=True) mock_device.available = True mock_device.name = NAME mock_device.info = DeviceInfo( MANUFACTURER, MODEL, FIRMWARE_VERSION, SERIAL_NUMBER, MAC, ) mock_device.capabilities = Capabilities() mock_device.profiles = []

22db21b9d4a7c5c2a79d43d8edf5295aeb99c13d

https://github.com/home-assistant/core.git

def setup_mock_device(mock_device): mock_device.async_setup = AsyncMock(return_value=True) mock_device.a

setup_mock_device

tests/sentry/integrations/msteams/test_message_builder.py

test(msteams): Add tests for building group card (#36834) Add tests for build_group_card which builds issues cards. Does NOT test all visual aspects of the card. Only ensures that certain important elements are present and the basic structure of the card is correct.

sentry

Python

test_message_builder.py

def test_resolved_issue_message(self): self.group1.status = GroupStatus.RESOLVED self.group1.save() issue_card = build_group_card( group=self.group1, event=self.event1, rules=self.rules, integration=self.integration ) action_set = issue_card["body"][2]["items"][0] resolve_action = action_set["actions"][0] assert ActionType.SUBMIT == resolve_action["type"] assert "Unresolve" == resolve_action["title"]

db35e231ceababe8c9f5ca7b5d2ca685f07c7d5b

https://github.com/getsentry/sentry.git

def test_resolved_issue_message(self): self.group1.status =

test_resolved_issue_message

tests/models/mvp/test_modeling_mvp.py

Add MVP model (#17787) * Add MVP model * Update README * Remove useless module * Update docs * Fix bugs in tokenizer * Remove useless test * Remove useless module * Update vocab * Remove specifying * Remove specifying * Add #Copied ... statement * Update paper link * Remove useless TFMvp * Add #Copied ... statement * Fix style in test mvp model * Fix some typos * Fix properties of unset special tokens in non verbose mode * Update paper link * Update MVP doc * Update MVP doc * Fix README * Fix typos in docs * Update docs

transformers

Python

test_modeling_mvp.py

def test_shift_tokens_right(self): input_ids = torch.tensor([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]], dtype=torch.long) shifted = shift_tokens_right(input_ids, 1, 2) n_pad_before = input_ids.eq(1).float().sum() n_pad_after = shifted.eq(1).float().sum() self.assertEqual(shifted.shape, input_ids.shape) self.assertEqual(n_pad_after, n_pad_before - 1) self.assertTrue(torch.eq(shifted[:, 0], 2).all())

3cff4cc58730409c68f8afa2f3b9c61efa0e85c6

https://github.com/huggingface/transformers.git

def test_shift_tokens_right(self): input_ids = torch.tensor([[71, 82, 18, 33,

test_shift_tokens_right

python3.10.4/Lib/distutils/tests/test_archive_util.py

add python 3.10.4 for windows

XX-Net

Python

test_archive_util.py

def test_make_archive_tar(self): base_dir = self._create_files() base_name = os.path.join(self.mkdtemp() , 'archive') res = make_archive(base_name, 'tar', base_dir, 'dist') self.assertTrue(os.path.exists(res)) self.assertEqual(os.path.basename(res), 'archive.tar') self.assertEqual(self._tarinfo(res), self._created_files)

8198943edd73a363c266633e1aa5b2a9e9c9f526

https://github.com/XX-net/XX-Net.git

def test_make_archive_tar(self): base_dir = self._create_files() base_name = os.path.join(self.mkdtemp() , 'archive') res = make_archive(base_name, 'tar', base_dir, 'dist') self.assertTrue(os.path.exists(res)) self.assertEqual(os.path.basename(res), 'archive.tar') self.assertEqual(self._tarinfo(res), self._created_files)

test_make_archive_tar

homeassistant/components/zha/lock.py

Bump zigpy to 0.44.1 and zha-quirks to 0.0.69 (#68921) * Make unit tests pass * Flip response type check to not rely on it being a list https://github.com/zigpy/zigpy/pull/716#issuecomment-1025236190 * Bump zigpy and quirks versions to ZCLR8 releases * Fix renamed zigpy cluster attributes * Handle the default response for ZLL `get_group_identifiers` * Add more error context to `stage failed` errors * Fix unit test returning lists as ZCL request responses * Always load quirks when testing ZHA * Bump zha-quirks to 0.0.69

core

Python

lock.py

async def async_lock(self, **kwargs): result = await self._doorlock_channel.lock_door() if isinstance(result, Exception) or result[0] is not Status.SUCCESS: self.error("Error with lock_door: %s", result) return self.async_write_ha_state()

0f6296e4b520ec8daf0f12e7b6db3c863c811ae8

https://github.com/home-assistant/core.git

async def async_lock(self, **kwargs): result = await self._doorlock_channel.lock_door() if isinstance(result, Exception) or result[0] is not Status.SUCCESS:

async_lock

src/transformers/utils/fx.py

Traced models serialization and torchscripting fix (#17206) * Fix torch.jit.script and pickling issues * Fix get_attr issues * Fix import in function * Fix GPT-J and T5 tracing for torch=1.11 * Gate graph surgery on torch version * Modeling minor changes to enable TorchScripting * Model serialization / deserialization test * Remove _assert_is_none users

transformers

Python

fx.py

def __setitem__(self, indices, values): return self.tracer.create_proxy("call_function", operator.setitem, (self, indices, values), {})

2e7e4280aa6f380a4e3afad6524295a17901c56c

https://github.com/huggingface/transformers.git

def __setitem__(self, indices, values): return self.tracer.create_proxy("call_function", operator.setitem, (self, in

__setitem__

keras/optimizers/optimizers_test.py

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

keras

Python

optimizers_test.py

def test_rmsprop(self): with self.cached_session(): self._test_optimizer(optimizer_v1.RMSprop()) self._test_optimizer(optimizer_v1.RMSprop(decay=1e-3))

84afc5193d38057e2e2badf9c889ea87d80d8fbf

https://github.com/keras-team/keras.git

def test_rmsprop(self): with self.cached_session(): self._te

test_rmsprop

homeassistant/components/ws66i/media_player.py

Add ws66i core integration (#56094) * Add ws66i core integration * Remove all ws66i translations * Update ws66i unit tests to meet minimum code coverage * Update ws66i based on @bdraco review * General improvements after 2nd PR review * Disable entities if amp shutoff, set default source names, set 30sec polling * Add _attr_ and change async_on_unload * Improve entity generation * Implement coordinator * Made options fields required, retry connection on failed attempts, use ZoneStatus for attributes * Refactor WS66i entity properties, raise HomeAssistantError on restore service if no snapshot * Update to pyws66i v1.1 * Add quality scale of silver to manifest * Update config_flow test

core

Python

media_player.py

async def async_set_volume_level(self, volume): await self.hass.async_add_executor_job( self._ws66i.set_volume, self._zone_id, int(volume * MAX_VOL) ) self._status.volume = int(volume * MAX_VOL) self._async_update_attrs_write_ha_state()

5e737bfe4fbc5a724f5fdf04ea9319c2224cb114

https://github.com/home-assistant/core.git

async def async_set_volume_level(self, volume): await self.hass.async_add_executor_job( self._ws66i.set_volume, self._zone_id, int(

async_set_volume_level

rllib/examples/two_trainer_workflow.py

[RLlib] Examples folder: All `training_iteration` translations. (#23712)

ray

Python

two_trainer_workflow.py

def get_default_config(cls) -> TrainerConfigDict: # Run this Trainer with new `training_iteration` API and set some PPO-specific # parameters. return with_common_config( { "_disable_execution_plan_api": True, "num_sgd_iter": 10, "sgd_minibatch_size": 128, } )

434265edd0926f7838cca4dbce00e88149e6bbf0

https://github.com/ray-project/ray.git

def get_default_config(cls) -> TrainerConfigDict: # Run this Trainer with new `training_iteration` API and set some PPO-specific # parameters. return with_common_config(

get_default_config

python/ray/_private/runtime_env/utils.py

[1/n][cross-language runtime env] runtime env protobuf refactor (#21551) We need to support runtime env for java、c++ and cross-language. This PR only do a refactor of protobuf. Related issue #21731

ray

Python

utils.py

def has_conda(self) -> str: return self._proto_runtime_env.python_runtime_env.HasField( "conda_runtime_env")

f8e41215b3fc8f45660e6afac4fe6faad73287f4

https://github.com/ray-project/ray.git

def has_conda(self) -> str: return self._proto_runtime_env.python_runtime_env.HasField( "conda_runtime_en

has_conda

erpnext/regional/report/eway_bill/eway_bill.py

style: format code with black

erpnext

Python

eway_bill.py

def get_data(filters): conditions = get_conditions(filters) data = frappe.db.sql( % conditions, as_dict=1, ) unit = { "Bag": "BAGS", "Bottle": "BOTTLES", "Kg": "KILOGRAMS", "Liter": "LITERS", "Meter": "METERS", "Nos": "NUMBERS", "PKT": "PACKS", "Roll": "ROLLS", "Set": "SETS", } # Regular expression set to remove all the special characters special_characters = r"[$%^*()+\\[\]{};':\"\\|<>.?]" for row in data: set_defaults(row) set_taxes(row, filters) set_address_details(row, special_characters) # Eway Bill accepts date as dd/mm/yyyy and not dd-mm-yyyy row.posting_date = "/".join(str(row.posting_date).replace("-", "/").split("/")[::-1]) row.lr_date = "/".join(str(row.lr_date).replace("-", "/").split("/")[::-1]) if row.gst_vehicle_type == "Over Dimensional Cargo (ODC)": row.gst_vehicle_type = "ODC" row.item_name = re.sub(special_characters, " ", row.item_name) row.description = row.item_name row.uom = unit.get(row.uom, row.uom) # For removing special charactes and numbers from customer. row.customer = re.sub(special_characters[:-1] + "&0-9" + "]", "", row.customer) return data

494bd9ef78313436f0424b918f200dab8fc7c20b

https://github.com/frappe/erpnext.git

def get_data(filters): conditions = get_conditions(filters) data = frappe.db.sql( % conditions, as_dict=1, ) unit = { "Bag": "BAGS", "Bottle": "BOTTLES", "Kg": "KILOGRAMS", "Liter": "LITERS", "Meter": "METERS", "Nos": "NUMBERS", "PKT": "PACKS", "Roll": "ROLLS", "Set": "SETS", } # Regular expression set to remove all the special characters special_characters

get_data

tests/sentry/replays/test_organization_issue_replay_count.py

feat(replays): Add issue replay count endpoint (#41996) ## What this PR does We create an endpoint that provides accurate counts of replay_ids associated with issue ids within a timeframe. The endpoint will return a format like so: ``` { issue_id_1: 1, issue_id_2: 20, etc. etc. } ``` ### Constraints - Between 1 and 25 issue_ids may be passed in (25 being the current pagination size of the issue stream) - We will only count up to 50 replay_ids, 50 chosen somewhat arbitrarily but seems a reasonable size. - We will only retrieve up to 100 replay_ids per issue_id. this means that if over half of replay_ids are sampled, it's possible that we may undercount replays as we will miss some. This is a small edge case and is acceptable. ## Modifications: - We modify discover.py to allow for private_use of the `groupArray` clickhouse function - In our endpoint we use QueryBuilder to hit the discover dataset, then query the replays dataset with the replay_ids returned - We then count up each replay_id confirmed to exist by its associated issue_id, and return the resulting dict ### Why are we doing this? Because of sampling / dropped data, events can have replay_ids that don't exist. this is nominally fine, although it results in missing counts / the product not seeming trustworthy in places. Fixes https://github.com/getsentry/replay-backend/issues/190

sentry

Python

test_organization_issue_replay_count.py

def setUp(self): super().setUp() self.min_ago = before_now(minutes=1) self.login_as(user=self.user) self.url = reverse( "sentry-api-0-organization-issue-replay-count", kwargs={"organization_slug": self.project.organization.slug}, ) self.features = {"organizations:session-replay": True}

6e2d3d461e9638981b6619952f59f78e44a93917

https://github.com/getsentry/sentry.git

def setUp(self): super().setUp() self.min_ago = before_now(minutes=1) self.login_as(user=self.user) self.url = reverse( "sentry-api-0-organization-issue-replay-count",

setUp

tests/models/opt/test_modeling_opt.py

Fix expected value for OPT test `test_inference_no_head` (#17395) * Fix expected value * 5e-5 Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

transformers

Python

test_modeling_opt.py

def test_inference_no_head(self): model = OPTModel.from_pretrained("facebook/opt-350m").to(torch_device) input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]) with torch.no_grad(): output = model(input_ids=input_ids).last_hidden_state expected_shape = torch.Size((1, 11, 512)) self.assertEqual(output.shape, expected_shape) # expected value works for CPU, as well as GPU (with TF32 disabled) expected_slice = torch.tensor( [ [-0.28726277, -1.9241608, -0.3058734], [-1.2737825, -0.13332152, -0.18766522], [0.41159445, 0.1191957, -1.3107123], ], device=torch_device, ) assert_tensors_close(output[0, :3, :3], expected_slice, atol=5e-5) @require_torch @slow

4d727bd2dff377caeab21ff4e1bf4b26c2397c8a

@require_torch @slow

https://github.com/huggingface/transformers.git

def test_inference_no_head(self): model = OPTModel.from_pretrained("facebook/opt-350m").to(torch_device) i

test_inference_no_head

jina/parsers/orchestrate/runtimes/remote.py

docs: fix port and protocol description for the gateway (#5456) Co-authored-by: Jina Dev Bot <dev-bot@jina.ai>

jina

Python

remote.py

def mixin_gateway_protocol_parser(parser): from jina.enums import GatewayProtocolType parser.add_argument( '--protocol', '--protocols', nargs='+', type=GatewayProtocolType.from_string, choices=list(GatewayProtocolType), default=[GatewayProtocolType.GRPC], help=f'Communication protocol of the server exposed by the Gateway. This can be a single value or a list of protocols, depending on your chosen Gateway. Choose the convenient protocols from: {[protocol.to_string() for protocol in list(GatewayProtocolType)]}.', )

47da80beca9cd60db51081594f1fcc5bee6d1246

https://github.com/jina-ai/jina.git

def mixin_gateway_protocol_parser(parser): from jina.enums import GatewayProtocolType parser.add_argument( '--protocol', '--protocols', nargs='+', type=GatewayProtocolType.from_string, choices=l

mixin_gateway_protocol_parser

tests/test_console.py

Test to ensure NullFile set as Console.file when stdout null

rich

Python

test_console.py

def test_console_null_file(monkeypatch): # When stdout and stderr are null, Console.file should be replaced with NullFile monkeypatch.setattr("sys.stdout", None) monkeypatch.setattr("sys.stderr", None) console = Console() assert isinstance(console.file, NullFile)

97bffbc7b1640dfc7bc20809dc0b9d1b536d7644

https://github.com/Textualize/rich.git

def test_console_null_file(monkeypatch): # When stdout and stderr are null, Console.file should be replaced with NullFile monkeypatch.setattr("sys.stdout", None)

test_console_null_file

wagtail/admin/tests/pages/test_delete_page.py

Reformat with black

wagtail

Python

test_delete_page.py

def setUp(self): # Find root page self.root_page = Page.objects.get(id=2) # Add child page self.child_page = SimplePage( title="Hello world!", slug="hello-world", content="hello" ) self.root_page.add_child(instance=self.child_page) # Add a page with child pages of its own self.child_index = StandardIndex(title="Hello index", slug="hello-index") self.root_page.add_child(instance=self.child_index) self.grandchild_page = StandardChild(title="Hello Kitty", slug="hello-kitty") self.child_index.add_child(instance=self.grandchild_page) # Login self.user = self.login()

d10f15e55806c6944827d801cd9c2d53f5da4186

https://github.com/wagtail/wagtail.git

def setUp(self): # Find root page self.root_page = Page.objects.get(id=2) # Add child page self.child_page = SimplePage( title="Hello world!", slug="hello-world", content="hello" ) self.root_page.add_child(instance=self.child_page) # Add a page with child pages of its own self.child_index = StandardIndex(title="Hello index", slug="hello-index") self.root_page.add_child(instance=self

setUp

tests/test_client.py

Fix bug where log workers would access a cached client across event loops

prefect

Python

test_client.py

async def test_client_can_opt_out_of_lifespan_management(self): startup, shutdown = MagicMock(), MagicMock() app = FastAPI(on_startup=[startup], on_shutdown=[shutdown]) client = OrionClient(app, manage_ephemeral_lifespan=False)

ef229fdb02297bcfff6aa95f210ce73e35074b99

https://github.com/PrefectHQ/prefect.git

async def test_client_can_opt_out_of_lifespan_management(self): startup, shutdown = MagicMock(), MagicMock() app = FastAPI(on_startup=[startup], on_shutdown=[shutdown]

test_client_can_opt_out_of_lifespan_management

pandas/tests/resample/test_base.py

TST: Use more xfail instead of skip (#45719)

pandas

Python

test_base.py

def test_resample_empty_series(freq, empty_series_dti, resample_method, request): # GH12771 & GH12868 if resample_method == "ohlc" and isinstance(empty_series_dti.index, PeriodIndex): request.node.add_marker( pytest.mark.xfail( reason=f"GH13083: {resample_method} fails for PeriodIndex" ) ) ser = empty_series_dti result = getattr(ser.resample(freq), resample_method)() expected = ser.copy() expected.index = _asfreq_compat(ser.index, freq) tm.assert_index_equal(result.index, expected.index) assert result.index.freq == expected.index.freq tm.assert_series_equal(result, expected, check_dtype=False) @all_ts @pytest.mark.parametrize("freq", ["M", "D", "H"])

bfe2d528a9398679acf05ffcdc60d3c181e0f17e

@all_ts @pytest.mark.parametrize("freq", ["M", "D", "H"])

https://github.com/pandas-dev/pandas.git

def test_resample_empty_series(freq, empty_series_dti, resample_method, request): # GH12771 & GH12868 if resample_method == "ohlc" and i

test_resample_empty_series

torch/testing/_comparison.py

make meta tensor data access error message for expressive in assert_close (#68802) Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/68802 Without this patch, the error message of comparing meta tensors looks like this after #68722 was merged: ```python >>> t = torch.empty((), device="meta") >>> assert_close(t, t) NotImplementedError: Could not run 'aten::abs.out' with arguments from the 'Meta' backend. [...] [...] The above exception was the direct cause of the following exception: [...] RuntimeError: Comparing TensorLikePair( id=(), actual=tensor(..., device='meta', size=()), expected=tensor(..., device='meta', size=()), rtol=1.3e-06, atol=1e-05, equal_nan=False, check_device=True, check_dtype=True, check_layout=True, check_stride=False, check_is_coalesced=True, ) resulted in the unexpected exception above. If you are a user and see this message during normal operation please file an issue at https://github.com/pytorch/pytorch/issues. If you are a developer and working on the comparison functions, please except the previous error and raise an expressive `ErrorMeta` instead. ``` Thus, we follow our own advice and turn it into an expected exception until #68592 is resolved: ```python >>> t = torch.empty((), device="meta") >>> assert_close(t, t) ValueError: Comparing meta tensors is currently not supported ``` Test Plan: Imported from OSS Reviewed By: ngimel Differential Revision: D33542999 Pulled By: mruberry fbshipit-source-id: 0fe1ddee15b5decdbd4c5dd84f03804ca7eac95b

pytorch

Python

_comparison.py

def _handle_meta_tensor_data_access(self): try: yield except NotImplementedError as error: if "meta" not in str(error).lower(): raise error # TODO: See https://github.com/pytorch/pytorch/issues/68592 raise self._make_error_meta(NotImplementedError, "Comparing meta tensors is currently not supported.")

8d05174defd689cb1cb2346e0cde5b7fa572814a

https://github.com/pytorch/pytorch.git

def _handle_meta_tensor_data_access(self): try: yield except NotImplementedError as error: if "meta" not in str(error).lower(): raise error # TODO: See https://github.com/pytorch/pytorch/issues/68592 raise self._make_error_meta(NotImplementedError, "Compa

_handle_meta_tensor_data_access

homeassistant/components/auth/login_flow.py

Add FlowResultType enum to data entry flow (#72955)

core

Python

login_flow.py

async def _async_flow_result_to_response(self, request, client_id, result): if result["type"] != data_entry_flow.FlowResultType.CREATE_ENTRY: # @log_invalid_auth does not work here since it returns HTTP 200. # We need to manually log failed login attempts. if ( result["type"] == data_entry_flow.FlowResultType.FORM and (errors := result.get("errors")) and errors.get("base") in ( "invalid_auth", "invalid_code", ) ): await process_wrong_login(request) return self.json(_prepare_result_json(result)) result.pop("data") hass: HomeAssistant = request.app["hass"] result_obj: Credentials = result.pop("result") # Result can be None if credential was never linked to a user before. user = await hass.auth.async_get_user_by_credentials(result_obj) if user is not None and ( user_access_error := async_user_not_allowed_do_auth(hass, user) ): return self.json_message( f"Login blocked: {user_access_error}", HTTPStatus.FORBIDDEN ) await process_success_login(request) result["result"] = self._store_result(client_id, result_obj) return self.json(result)

f91aa33c5f7bc13ed031a95f946f70e11af1e2f3

https://github.com/home-assistant/core.git

async def _async_flow_result_to_response(self, request, client_id, result): if result["type"] != data_entry_flow.FlowResultType.CREATE_ENTRY: # @log_invalid_auth does not work here since it returns HTTP 200. # We need to manually log failed login attempts. if ( result["type"] == data_entry_flow.FlowResultType.FORM and (errors := result.get("errors")) and errors.get("base") in ( "invalid_auth", "invalid_code", ) ): await process_wrong_login(request) return self.json(_prepare_result_json(result)) result.pop("data") hass: HomeAssistant = request.app["hass"] result_obj: Credentials = result.pop("result") # Result can be None if credential was never linked to a user before. user = await hass.auth.async_get_user_by_credentials(result_obj) if user is not None and ( user_access_error := async_user_not_allowed_do_auth(hass, user)

_async_flow_result_to_response

homeassistant/components/avea/light.py

Improve type hints in light [a-i] (#75936) * Improve type hints in ads light * Improve type hints in avea light * Improve type hints in avion light * Improve type hints in broadlink light * More type hints * One more

core

Python

light.py

def update(self) -> None: if (brightness := self._light.get_brightness()) is not None: self._attr_is_on = brightness != 0 self._attr_brightness = round(255 * (brightness / 4095))

20fec104e2a11b1a5164d7fe779eb0d894e098cf

https://github.com/home-assistant/core.git

def update(self) -> None:

update

src/documents/consumer.py

When raising an exception during exception handling, chain them together for slightly cleaner logs

paperless-ngx

Python

consumer.py

def run_post_consume_script(self, document): if not settings.POST_CONSUME_SCRIPT: return if not os.path.isfile(settings.POST_CONSUME_SCRIPT): self._fail( MESSAGE_POST_CONSUME_SCRIPT_NOT_FOUND, f"Configured post-consume script " f"{settings.POST_CONSUME_SCRIPT} does not exist.", ) self.log( "info", f"Executing post-consume script {settings.POST_CONSUME_SCRIPT}", ) script_env = os.environ.copy() script_env["DOCUMENT_ID"] = str(document.pk) script_env["DOCUMENT_CREATED"] = str(document.created) script_env["DOCUMENT_MODIFIED"] = str(document.modified) script_env["DOCUMENT_ADDED"] = str(document.added) script_env["DOCUMENT_FILE_NAME"] = document.get_public_filename() script_env["DOCUMENT_SOURCE_PATH"] = os.path.normpath(document.source_path) script_env["DOCUMENT_ARCHIVE_PATH"] = os.path.normpath( str(document.archive_path), ) script_env["DOCUMENT_THUMBNAIL_PATH"] = os.path.normpath( document.thumbnail_path, ) script_env["DOCUMENT_DOWNLOAD_URL"] = reverse( "document-download", kwargs={"pk": document.pk}, ) script_env["DOCUMENT_THUMBNAIL_URL"] = reverse( "document-thumb", kwargs={"pk": document.pk}, ) script_env["DOCUMENT_CORRESPONDENT"] = str(document.correspondent) script_env["DOCUMENT_TAGS"] = str( ",".join(document.tags.all().values_list("name", flat=True)), ) try: Popen( ( settings.POST_CONSUME_SCRIPT, str(document.pk), document.get_public_filename(), os.path.normpath(document.source_path), os.path.normpath(document.thumbnail_path), reverse("document-download", kwargs={"pk": document.pk}), reverse("document-thumb", kwargs={"pk": document.pk}), str(document.correspondent), str(",".join(document.tags.all().values_list("name", flat=True))), ), env=script_env, ).wait() except Exception as e: self._fail( MESSAGE_POST_CONSUME_SCRIPT_ERROR, f"Error while executing post-consume script: {e}", exc_info=True, exception=e, )

b70e21a6d50bfc84e76fd68ce0b8c22b0928ff51

https://github.com/paperless-ngx/paperless-ngx.git

def run_post_consume_script(self, document): if not settings.POST_CONSUME_SCRIPT: return if not os.path.isfile(settings.POST_CONSUME_SCRIPT): self._fail( MESSAGE_POST_CONSUME_SCRIPT_NOT_FOUND, f"Configured post-consume script " f"{settings.POST_CONSUME_SCRIPT} does not exist.", ) self.log( "info", f"Executing post-consume script {settings.POST_CONSUME_SCRIPT}", ) script_env = os.environ.copy() script_env["DOCUMENT_ID"] = str(document.pk) script_env["DOCUMENT_CREATED"] = str(document.created) script_env["DOCUMENT_MODIFIED"] = str(document.modified) script_env["DOCUMENT_ADDED"] = str(document.added) script_env["DOCUMENT_FILE_NAME"] = document.get_public_filename() script_env["DOCUMENT_SOURCE_PATH"] = os.path.normpath(document.source_path)

run_post_consume_script

tests/sentry/snuba/metrics/test_metrics_layer/test_metrics_enhanced_performance.py

feat(metrics): Add support for wildcard searches (#41114)

sentry

Python

test_metrics_enhanced_performance.py

def test_broken_custom_metric(self, mock): # Store valid metric self.store_transaction_metric( 1, metric="measurements.something_custom", internal_metric="d:transactions/measurements.something_custom@millisecond", entity="metrics_distributions", timestamp=self.day_ago + timedelta(hours=1, minutes=0), ) # mock mri failing to parse the metric mock.return_value = None result = get_custom_measurements( project_ids=[self.project.id], organization_id=self.organization.id, start=self.day_ago, ) assert result == []

f322fa798595b3bc7dc54e904c9628e44102f1f5

https://github.com/getsentry/sentry.git

def test_broken_custom_metric(self, mock): # Store valid metric self.store_transaction_metric( 1, metric="measurements.something_custom", internal_metric="d:transactions/measurements.something_custom@millisecond", entity="metrics_distributions", timestamp=self.day_ago + timedelta(hours=1, minutes=0), ) # mock mri failing to parse the metric mock.return_value = None result = get_custom_measurements( project_ids=[self.project.id], organiz

test_broken_custom_metric

tests/components/wemo/conftest.py

Use properties of wemo Maker device (#72378)

core

Python

conftest.py

def create_pywemo_device(pywemo_registry, pywemo_model): cls = getattr(pywemo, pywemo_model) device = create_autospec(cls, instance=True) device.host = MOCK_HOST device.port = MOCK_PORT device.name = MOCK_NAME device.serialnumber = MOCK_SERIAL_NUMBER device.model_name = pywemo_model.replace("LongPress", "") device.udn = f"uuid:{device.model_name}-1_0-{device.serialnumber}" device.firmware_version = MOCK_FIRMWARE_VERSION device.get_state.return_value = 0 # Default to Off device.supports_long_press.return_value = cls.supports_long_press() if issubclass(cls, pywemo.Insight): device.get_standby_state = pywemo.StandbyState.OFF device.current_power_watts = MOCK_INSIGHT_CURRENT_WATTS device.today_kwh = MOCK_INSIGHT_TODAY_KWH device.threshold_power_watts = MOCK_INSIGHT_STATE_THRESHOLD_POWER device.on_for = 1234 device.today_on_time = 5678 device.total_on_time = 9012 if issubclass(cls, pywemo.Maker): device.has_sensor = 1 device.sensor_state = 1 device.switch_mode = 1 device.switch_state = 0 url = f"http://{MOCK_HOST}:{MOCK_PORT}/setup.xml" with patch("pywemo.setup_url_for_address", return_value=url), patch( "pywemo.discovery.device_from_description", return_value=device ): yield device @pytest.fixture(name="pywemo_device")

92582beeff7a5d1e9fa6cfae3afa41f596b5e3c2

@pytest.fixture(name="pywemo_device")

https://github.com/home-assistant/core.git

def create_pywemo_device(pywemo_registry, pywemo_model): cls = getattr(pywemo, pywemo_model) device = create_autospec(cls, instance=True) device.host = MOCK_HOST device.port = MOCK_PORT device.name = MOCK_NAME device.serialnumber = MOCK_SERIAL_NUMBER device.model_name = pywemo_model.replace("LongPress", "") device.udn = f"uuid:{device.model_name}-1_0-{device.serialnumber}" devic

create_pywemo_device

homeassistant/components/mqtt/vacuum/schema_state.py

Use `_attr_` for MQTT vacuum (#81534) * Use `_attr_` for MQTT vacuum * Remove unneeded properties * Follow-up comment * Remove default value

core

Python

schema_state.py

async def async_locate(self, **kwargs): if self.supported_features & VacuumEntityFeature.LOCATE == 0: return await self.async_publish( self._command_topic, self._config[CONF_PAYLOAD_LOCATE], self._config[CONF_QOS], self._config[CONF_RETAIN], self._config[CONF_ENCODING], )

b364ef98a073214aad8deff4ff9b91e9ff041557

https://github.com/home-assistant/core.git

async def async_locate(self, **kwargs): if self.sup

async_locate

airbyte-integrations/connectors/source-google-analytics-v4/unit_tests/unit_test.py

reintroduce window in days, log warning when sampling occurs (#9480) * reintroduce window in days, log warning when sampling occurs * Unit tests * Documentation update * Update airbyte-integrations/connectors/source-google-analytics-v4/source_google_analytics_v4/source.py Co-authored-by: Sergei Solonitcyn <11441558+sergei-solonitcyn@users.noreply.github.com> * fix the spec Signed-off-by: Sergei Solonitcyn <sergei.solonitcyn@zazmic.com> * some mypy fixes Signed-off-by: Sergei Solonitcyn <sergei.solonitcyn@zazmic.com> * bump version * format * updated spec and def yaml * Update source.py Co-authored-by: Sergei Solonitcyn <11441558+sergei-solonitcyn@users.noreply.github.com> Co-authored-by: Sergei Solonitcyn <sergei.solonitcyn@zazmic.com> Co-authored-by: auganbay <auganenu@gmail.com>

airbyte

Python

unit_test.py

def mock_auth_check_connection(requests_mock): yield requests_mock.post( "https://analyticsreporting.googleapis.com/v4/reports:batchGet", json={"data": {"test": "value"}}, ) @pytest.fixture

f78ede0b511de022482c5f0713752ddf01460eb4

@pytest.fixture

https://github.com/airbytehq/airbyte.git

def mock_auth_check_connection(requests_mock): yield requests_mock.post( "https://analyticsreporting.googleapis.com/v4/reports:batchGet", json={"data": {"test": "value"}}, ) @pytest.fixture

mock_auth_check_connection

rllib/models/utils.py

[CI] Format Python code with Black (#21975) See #21316 and #21311 for the motivation behind these changes.

ray

Python

utils.py

def get_filter_config(shape): # VizdoomGym (large 480x640). filters_480x640 = [ [16, [24, 32], [14, 18]], [32, [6, 6], 4], [256, [9, 9], 1], ] # VizdoomGym (small 240x320). filters_240x320 = [ [16, [12, 16], [7, 9]], [32, [6, 6], 4], [256, [9, 9], 1], ] # 96x96x3 (e.g. CarRacing-v0). filters_96x96 = [ [16, [8, 8], 4], [32, [4, 4], 2], [256, [11, 11], 2], ] # Atari. filters_84x84 = [ [16, [8, 8], 4], [32, [4, 4], 2], [256, [11, 11], 1], ] # Small (1/2) Atari. filters_42x42 = [ [16, [4, 4], 2], [32, [4, 4], 2], [256, [11, 11], 1], ] # Test image (10x10). filters_10x10 = [ [16, [5, 5], 2], [32, [5, 5], 2], ] shape = list(shape) if len(shape) in [2, 3] and (shape[:2] == [480, 640] or shape[1:] == [480, 640]): return filters_480x640 elif len(shape) in [2, 3] and (shape[:2] == [240, 320] or shape[1:] == [240, 320]): return filters_240x320 elif len(shape) in [2, 3] and (shape[:2] == [96, 96] or shape[1:] == [96, 96]): return filters_96x96 elif len(shape) in [2, 3] and (shape[:2] == [84, 84] or shape[1:] == [84, 84]): return filters_84x84 elif len(shape) in [2, 3] and (shape[:2] == [42, 42] or shape[1:] == [42, 42]): return filters_42x42 elif len(shape) in [2, 3] and (shape[:2] == [10, 10] or shape[1:] == [10, 10]): return filters_10x10 else: raise ValueError( "No default configuration for obs shape {}".format(shape) + ", you must specify `conv_filters` manually as a model option. " "Default configurations are only available for inputs of shape " "[42, 42, K] and [84, 84, K]. You may alternatively want " "to use a custom model or preprocessor." )

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

https://github.com/ray-project/ray.git

def get_filter_config(shape): # VizdoomGym (large 480x640). filters_480x640 = [ [16, [24, 32], [14, 18]], [32, [6, 6], 4], [256, [9, 9], 1], ] # VizdoomGym (small 240x320). filters_240x320 = [ [16, [12, 16], [7, 9]], [32, [6, 6], 4], [256, [9, 9], 1], ] # 96x96x3 (e.g. CarRacing-v0). filters_96x96 = [ [16, [8, 8], 4], [32, [4, 4], 2], [256, [11, 11], 2], ] # Atari. filters_84x84 = [ [16, [8, 8], 4], [32, [4, 4], 2], [256, [11, 11], 1], ] # Small (1/2) Atari. filters_42x42 = [ [16, [4, 4], 2], [32, [4, 4], 2], [256, [11, 11], 1], ] # Test image (10x10). filters_10x10 = [ [16, [5, 5], 2], [32, [5, 5], 2]

get_filter_config

django/db/backends/oracle/operations.py

Refs #33476 -- Reformatted code with Black.

django

Python

operations.py

def convert_empty_string(value, expression, connection): return "" if value is None else value

9c19aff7c7561e3a82978a272ecdaad40dda5c00

https://github.com/django/django.git

def convert_empty_string(value, expression, connection):

convert_empty_string

test/units/galaxy/test_collection_install.py

Add --offline option to 'ansible-galaxy collection install' (#78678) * Add --offline option to 'ansible-galaxy collection install' to prevent querying distribution servers This allows installing/upgrading individual tarfiles to have dependency resolution. Previously needed to be done manually with --no-deps or else all collections and dependencies needed to be included in the requirements. Co-authored-by: Sviatoslav Sydorenko <wk.cvs.github@sydorenko.org.ua>

ansible

Python

test_collection_install.py

def test_build_requirement_from_name_multiple_version_results(galaxy_server, monkeypatch, tmp_path_factory): test_dir = to_bytes(tmp_path_factory.mktemp('test-ÅÑŚÌβŁÈ Collections Input')) concrete_artifact_cm = collection.concrete_artifact_manager.ConcreteArtifactsManager(test_dir, validate_certs=False) multi_api_proxy = collection.galaxy_api_proxy.MultiGalaxyAPIProxy([galaxy_server], concrete_artifact_cm) dep_provider = dependency_resolution.providers.CollectionDependencyProvider(apis=multi_api_proxy, concrete_artifacts_manager=concrete_artifact_cm) matches = RequirementCandidates() mock_find_matches = MagicMock(side_effect=matches.func_wrapper(dep_provider.find_matches), autospec=True) monkeypatch.setattr(dependency_resolution.providers.CollectionDependencyProvider, 'find_matches', mock_find_matches) mock_get_info = MagicMock() mock_get_info.return_value = api.CollectionVersionMetadata('namespace', 'collection', '2.0.5', None, None, {}, None, None) monkeypatch.setattr(galaxy_server, 'get_collection_version_metadata', mock_get_info) mock_get_versions = MagicMock() mock_get_versions.return_value = ['1.0.1', '1.0.2', '1.0.3'] monkeypatch.setattr(galaxy_server, 'get_collection_versions', mock_get_versions) mock_get_versions.return_value = ['2.0.0', '2.0.1', '2.0.2', '2.0.3', '2.0.4', '2.0.5'] monkeypatch.setattr(galaxy_server, 'get_collection_versions', mock_get_versions) cli = GalaxyCLI(args=['ansible-galaxy', 'collection', 'install', 'namespace.collection:!=2.0.2']) requirements = cli._require_one_of_collections_requirements( ['namespace.collection:!=2.0.2'], None, artifacts_manager=concrete_artifact_cm )['collections'] actual = collection._resolve_depenency_map( requirements, [galaxy_server], concrete_artifact_cm, None, False, True, False, False, False)['namespace.collection'] assert actual.namespace == u'namespace' assert actual.name == u'collection' assert actual.src == galaxy_server assert actual.ver == u'2.0.5' # should be ordered latest to earliest assert [c.ver for c in matches.candidates] == [u'2.0.5', u'2.0.4', u'2.0.3', u'2.0.1', u'2.0.0'] assert mock_get_versions.call_count == 1 assert mock_get_versions.mock_calls[0][1] == ('namespace', 'collection')

a02e22e902a69aeb465f16bf03f7f5a91b2cb828

https://github.com/ansible/ansible.git

def test_build_requirement_from_name_multiple_version_results(galaxy_server, monkeypatch, tmp_path_factory): test_dir = to_bytes(tmp_path_factory.mktemp('test-ÅÑŚÌβŁÈ Collections Input')) concrete_artifact_cm = collection.concrete_artifact_manager.ConcreteArtifactsManager(test_dir, validate_certs=False) multi_api_proxy = collection.galaxy_api_proxy.MultiGalaxyAPIProxy([galaxy_server], concrete_artifact_cm) dep_provider = dependency_resolution.providers.CollectionDependencyProvider(apis=multi_api_proxy, concrete_artifacts_manager=concrete_artifact_cm) matches = RequirementCandidates() mock_find_matches = MagicMock(side_effect=matches.func_wrapper(dep_provider.find_matches), autospec=True) monkeypatch.setattr(dependency_resolution.providers.CollectionDependencyProvider, 'find_matches', mock_find_match

test_build_requirement_from_name_multiple_version_results

setup.py

Implement numpy hack in setup.py to enable install under Poetry (#3363) * Closes #3362: Install issue poetry * get rid of redundant exception handling this code can never raise an exception, so we shouldn't be expecting them Co-authored-by: Michael Penkov <m@penkov.dev>

gensim

Python

setup.py

def finalize_options(self): build_ext.finalize_options(self) import builtins builtins.__NUMPY_SETUP__ = False import numpy self.include_dirs.append(numpy.get_include()) if need_cython(): import Cython.Build Cython.Build.cythonize(list(make_c_ext(use_cython=True)), language_level=3) Cython.Build.cythonize(list(make_cpp_ext(use_cython=True)), language_level=3)

3331b824d2e3f7a65f5df7903382a0c0a30dcc61

https://github.com/RaRe-Technologies/gensim.git

def finalize_options(self): build_ext.finalize_options(self) import builtins builtins.__NUMPY_SETUP__ = False import numpy self.include_dirs.append(numpy.get_include()) if need_cython(): import Cython.Build Cython.Build.cythonize(list(make_c_ext(use_cython=True)), language_level=3) Cython

finalize_options

tests/providers/google/cloud/hooks/test_translate.py

Extract ClientInfo to module level (#21554)

airflow

Python

test_translate.py

def test_translate_client_creation(self, mock_client, mock_get_creds): result = self.hook.get_conn() mock_client.assert_called_once_with(credentials=mock_get_creds.return_value, client_info=CLIENT_INFO) assert mock_client.return_value == result assert self.hook._client == result

1b568d73e1dfb838a3a0446e3a6063b9f27f04b8

https://github.com/apache/airflow.git

def test_translate_client_creation(self, mock_client, mock_get_creds): result = self.hook.get_conn() mock_client.assert_called_once_with(credentials=mock_get_creds.r

test_translate_client_creation

erpnext/hr/doctype/training_event/test_training_event.py

style: format code with black

erpnext

Python

test_training_event.py

def create_training_program(training_program): if not frappe.db.get_value("Training Program", training_program): frappe.get_doc( { "doctype": "Training Program", "training_program": training_program, "description": training_program, } ).insert()

494bd9ef78313436f0424b918f200dab8fc7c20b

https://github.com/frappe/erpnext.git

def create_training_program(training_program): if not frappe.db.get_value("Training Program", training_program): frappe.get_doc( { "doctype": "Training Program", "training_program": training_program,

create_training_program

pandas/tests/series/indexing/test_setitem.py

TST/REF: port coercion tests to SetitemCastingEquivalents (#45209)

pandas

Python

test_setitem.py

def assert_warns(self, request): # check that we issue a FutureWarning about timezone-matching if request.function.__name__ == "test_slice_key": key = request.getfixturevalue("key") if not isinstance(key, slice): # The test is a no-op, so no warning will be issued yield return exp_dtype = request.getfixturevalue("exp_dtype") val = request.getfixturevalue("val") if exp_dtype == object and isinstance(val, Timestamp) and val.tz is not None: with tm.assert_produces_warning(FutureWarning, match="mismatched timezone"): yield else: yield @pytest.mark.parametrize( "val,exp_dtype", [(Timedelta("12 day"), "timedelta64[ns]"), (1, object), ("x", object)], )

3111ff0317171f03c8ed2e3f7a153872c85d798f

@pytest.mark.parametrize( "val,exp_dtype", [(Timedelta("12 day"), "timedelta64[ns]"), (1, object), ("x", object)], )

https://github.com/pandas-dev/pandas.git

def assert_warns(self, request): # check that we issue a FutureWarning about timezone-matching if request.function.__name__ == "test_slice_key": key = request.getfixturevalue("key") if not isinstance(key, slice): # The test is a no-op, so no warning will be issued yield return exp_dtype = request.getfixturevalue("exp_dtype") val = request.getfixturevalue("val") if exp_dtype == object and isinstance(val, Timestamp) and val.tz is not None: wit

assert_warns

tests/components/telegram_bot/test_broadcast.py

Fix telegram broadcast (#69452)

core

Python

test_broadcast.py

async def test_setup(hass): assert await async_setup_component( hass, "telegram_bot", { "telegram_bot": { "platform": "broadcast", "api_key": "1234567890:ABC", "allowed_chat_ids": [1], } }, ) await hass.async_block_till_done() assert hass.services.has_service("telegram_bot", "send_message") is True

c765e11f55282530275396f8bdc837cb96259920

https://github.com/home-assistant/core.git

async def test_setup(hass): assert await async_setup_componen

test_setup

jax/_src/numpy/ufuncs.py

DOC: ensure that _wraps() generates correct links to wrapped functions

jax

Python

ufuncs.py

def _logical_op(np_op, bitwise_op): @_wraps(np_op, update_doc=False, module='numpy') @partial(jit, inline=True) def op(*args): zero = lambda x: lax.full_like(x, shape=(), fill_value=0) args = (x if dtypes.issubdtype(dtypes.dtype(x), np.bool_) else lax.ne(x, zero(x)) for x in args) return bitwise_op(*_promote_args(np_op.__name__, *args)) return op fabs = _one_to_one_unop(np.fabs, lax.abs, True) bitwise_not = _one_to_one_unop(np.bitwise_not, lax.bitwise_not) invert = _one_to_one_unop(np.invert, lax.bitwise_not) negative = _one_to_one_unop(np.negative, lax.neg) positive = _one_to_one_unop(np.positive, lambda x: x) floor = _one_to_one_unop(np.floor, lax.floor, True) ceil = _one_to_one_unop(np.ceil, lax.ceil, True) exp = _one_to_one_unop(np.exp, lax.exp, True) log = _one_to_one_unop(np.log, lax.log, True) expm1 = _one_to_one_unop(np.expm1, lax.expm1, True) log1p = _one_to_one_unop(np.log1p, lax.log1p, True) sin = _one_to_one_unop(np.sin, lax.sin, True) cos = _one_to_one_unop(np.cos, lax.cos, True) tan = _one_to_one_unop(np.tan, lax.tan, True) arcsin = _one_to_one_unop(np.arcsin, lax.asin, True) arccos = _one_to_one_unop(np.arccos, lax.acos, True) arctan = _one_to_one_unop(np.arctan, lax.atan, True) sinh = _one_to_one_unop(np.sinh, lax.sinh, True) cosh = _one_to_one_unop(np.cosh, lax.cosh, True) arcsinh = _one_to_one_unop(np.arcsinh, lax.asinh, True) tanh = _one_to_one_unop(np.tanh, lax.tanh, True) arctanh = _one_to_one_unop(np.arctanh, lax.atanh, True) sqrt = _one_to_one_unop(np.sqrt, lax.sqrt, True) cbrt = _one_to_one_unop(np.cbrt, lax.cbrt, True) add = _maybe_bool_binop(np.add, lax.add, lax.bitwise_or) bitwise_and = _one_to_one_binop(np.bitwise_and, lax.bitwise_and) bitwise_or = _one_to_one_binop(np.bitwise_or, lax.bitwise_or) bitwise_xor = _one_to_one_binop(np.bitwise_xor, lax.bitwise_xor) left_shift = _one_to_one_binop(np.left_shift, lax.shift_left) equal = _one_to_one_binop(np.equal, lax.eq) multiply = _maybe_bool_binop(np.multiply, lax.mul, lax.bitwise_and) not_equal = _one_to_one_binop(np.not_equal, lax.ne) subtract = _one_to_one_binop(np.subtract, lax.sub) arctan2 = _one_to_one_binop(np.arctan2, lax.atan2, True) minimum = _one_to_one_binop(np.minimum, lax.min) maximum = _one_to_one_binop(np.maximum, lax.max) float_power = _one_to_one_binop(np.float_power, lax.pow, True) nextafter = _one_to_one_binop(np.nextafter, lax.nextafter, True, True) greater_equal = _comparison_op(np.greater_equal, lax.ge) greater = _comparison_op(np.greater, lax.gt) less_equal = _comparison_op(np.less_equal, lax.le) less = _comparison_op(np.less, lax.lt) logical_and = _logical_op(np.logical_and, lax.bitwise_and) logical_not = _logical_op(np.logical_not, lax.bitwise_not) logical_or = _logical_op(np.logical_or, lax.bitwise_or) logical_xor = _logical_op(np.logical_xor, lax.bitwise_xor) @_wraps(np.arccosh, module='numpy') @jit

9769a0accf77c4e62ff3ace0f71b5c0697b35b54

@_wraps(np.arccosh, module='numpy') @jit

https://github.com/google/jax.git

def _logical_op(np_op, bitwise_op): @_wraps(np_op, update_doc=False, module='numpy') @partial(jit, inline=True) def op(*args): zero = lambda x: lax.full_like(x, shape=(), fill_value=0) args = (x if dtypes.issubdtype(dtypes.dtype(x), np.bool_) else lax.ne(x, zero(x)) for x in args) return bitwise_op(*_promote_args(np_op.__name__

_logical_op

saleor/plugins/webhook/tests/subscription_webhooks/payloads.py

Meta fields added to subscription webhooks event types. (#9759) * Meta fields added to subscription webhooks event types. * Imports adjustments. * Change Event type from Union to Interface. * Rebase fixes. * Review fixes * Handle AnonymousUser as requestor.

saleor

Python

payloads.py

def generate_app_payload(app, app_global_id): return json.dumps( { "app": { "id": app_global_id, "isActive": app.is_active, "name": app.name, "appUrl": app.app_url, } } )

f2ce999fa5865917b8d104d38ef3269eebaf6c06

https://github.com/saleor/saleor.git

def generate_app_payload(app, app_global_id): return json.dumps( { "app": { "id": app_global_id,

generate_app_payload

dashboard/modules/job/tests/test_job_agent.py

[Job Submission][refactor 4/N] Complete the remaining interfaces on JobAgent (#28533) Signed-off-by: Catch-Bull <burglarralgrub@gmail.com> just need to implement stop_job, and I remove get_job_info because we can access JobInfoStorage without call `ray.init`.

ray

Python

test_job_agent.py

async def test_submit_job(job_sdk_client, runtime_env_option, monkeypatch): # This flag allows for local testing of runtime env conda functionality # without needing a built Ray wheel. Rather than insert the link to the # wheel into the conda spec, it links to the current Python site. monkeypatch.setenv("RAY_RUNTIME_ENV_LOCAL_DEV_MODE", "1") agent_client, head_client = job_sdk_client runtime_env = runtime_env_option["runtime_env"] runtime_env = upload_working_dir_if_needed(runtime_env, logger=logger) runtime_env = upload_py_modules_if_needed(runtime_env, logger=logger) runtime_env = RuntimeEnv(**runtime_env_option["runtime_env"]).to_dict() request = validate_request_type( {"runtime_env": runtime_env, "entrypoint": runtime_env_option["entrypoint"]}, JobSubmitRequest, ) submit_result = await agent_client.submit_job_internal(request) job_id = submit_result.submission_id wait_for_condition( partial( _check_job, client=head_client, job_id=job_id, status=JobStatus.SUCCEEDED ), timeout=120, ) # There is only one node, so there is no need to replace the client of the JobAgent resp = await agent_client.get_job_logs_internal(job_id) assert runtime_env_option["expected_logs"] in resp.logs @pytest.mark.asyncio

8840be1942a69b2595a05c5c5556b0daec7abbcd

@pytest.mark.asyncio

https://github.com/ray-project/ray.git

async def test_submit_job(job_sdk_client, runtime_env_option, monkeypatch): # This flag allows for local testing of runtime env conda functionality # without needing a built Ray wheel. Rather than insert the link to the # wheel into the conda spec, it links to the current Python site. monkeypatch.setenv("RAY_RUNTIME_ENV_LOCAL_DEV_MODE", "1") agent_client, head_client = job_sdk_client runtime_env = runtime_env_option["runtime_env"] runtime_env = upload_working_dir_if_needed(runtime_env, logger=logger) runtime_env = upload_py_modules_if_needed(runtime_env, logger=logger) runtime_env = RuntimeEnv(**runtime_env_option["runtime_env"]).to_dict() request = validate_request_type( {"runtime_env": runtime_env, "entrypoint": runtime_env_option["entrypoint"]}, JobSubmitRequest, ) submit_result = await agent_client.submit_job_internal(request) job_id = submit_result.submission_id wait_for_condition( partial( _check_job, client=head_client, job_id=job_id, status=JobStatus.SUCCEEDED ), timeout=120, ) # There is only one node, so there is no need to replace the client of the

test_submit_job

networkx/algorithms/traversal/tests/test_dfs.py

MAINT: Update dfs_test with more comprehensive tests (#5654)

networkx

Python

test_dfs.py

def test_predecessor(self): assert nx.dfs_predecessors(self.G, source=0) == {1: 0, 2: 1, 3: 1, 4: 2} assert nx.dfs_predecessors(self.D) == {1: 0, 3: 2}

84aa3823e2904fd63178608373ffbed3096ae0d9

https://github.com/networkx/networkx.git

def test_predecessor(self): assert nx.dfs_predecessors(self.G, source=0) == {1: 0, 2: 1, 3: 1, 4: 2} assert nx.dfs_predecessors(self

test_predecessor

sympy/combinatorics/graycode.py

Updated import locations

sympy

Python

graycode.py

def graycode_subsets(gray_code_set): for bitstring in list(GrayCode(len(gray_code_set)).generate_gray()): yield get_subset_from_bitstring(gray_code_set, bitstring)

498015021131af4dbb07eb110e5badaba8250c7b

https://github.com/sympy/sympy.git

def graycode_subsets(gray_code_set): for bitstring in list(GrayCode(len(gray_code_set)).generate_gray()): yield get_subset_from_bitstring(gray_code_set, bitstring)

graycode_subsets

pandas/tests/plotting/test_series.py

TST: Clean tests/plotting (#45992)

pandas

Python

test_series.py

def test_plot_6951(self, ts): # GH 6951 ax = _check_plot_works(ts.plot, subplots=True) self._check_axes_shape(ax, axes_num=1, layout=(1, 1)) ax = _check_plot_works(ts.plot, subplots=True, layout=(-1, 1)) self._check_axes_shape(ax, axes_num=1, layout=(1, 1)) ax = _check_plot_works(ts.plot, subplots=True, layout=(1, -1)) self._check_axes_shape(ax, axes_num=1, layout=(1, 1))

03fef5f0e35200aa5828975b62782bcf11faa0d2

https://github.com/pandas-dev/pandas.git

def test_plot_6951(self, ts): # GH 6951 ax = _check_plot_works(ts.plot, subplots=True) self._check_axes_shape(ax, axes_num=1, layout=(1, 1))

test_plot_6951

wagtail/admin/ui/sidebar.py

Sidebar style updates (#8118) Co-authored-by: Thibaud Colas <thibaudcolas@gmail.com>

wagtail

Python

sidebar.py

def js_args(self): return [ reverse("wagtailadmin_home"), ] @adapter("wagtail.sidebar.SearchModule", base=BaseSidebarAdapter)

229fbf476aded9d5d5a6b32e1ad03025e8cbf392

@adapter("wagtail.sidebar.SearchModule", base=BaseSidebarAdapter)

https://github.com/wagtail/wagtail.git

def js_args(self): return [ reverse("wagtailadmin_home"), ] @adapter("wagtail.sidebar.SearchModule", base=B

js_args

lib/ansible/cli/doc.py

ansible-config added json/yaml output to list/dump (#77447) fixes #733644

ansible

Python

doc.py

def jdump(text): try: display.display(json_dump(text)) except TypeError as e: display.vvv(traceback.format_exc()) raise AnsibleError('We could not convert all the documentation into JSON as there was a conversion issue: %s' % to_native(e))

a12e0a0e874c6c0d18a1a2d83dcb106d207136af

https://github.com/ansible/ansible.git

def jdump(text): try: display.display(json_dump(text)) except TypeError as e: displa

jdump

test/mitmproxy/test_addonmanager.py

switch to stdlib logging mitmproxy previously used a homegrown logging mechanism based around `mitmproxy.ctx.log` and the `add_log` hook. This worked well for everything we control, but does not work outside the mitmproxy universe. For now we have simply ignored logging in e.g. tornado or h2, but with the upcoming introduction of mitmproxy_wireguard we now have a dependency on some Rust/PyO3 code for which we definitely want logs, but which also cannot easily be changed to use our homegrown logging (PyO3 does the heavy lifting to add interoperability with stdlib logging). Long story short, we want to introduce a log handler for stdlib logging. Now there are two ways how such a handler could operate: 1. We could build a handler that forwards all stdlib log events into our homegrown mechanism. 2. We embrace stdlib's logging as the correct way to do things, and get rid of our homegrown stuff. This PR follows the second approach by removing the `add_log` hook and rewriting the `TermLog` and `EventStore` addons to listen for stdlib log records. This means that all `mitmproxy.ctx.log.info` events are now simply `logging.info` etc. One upside of this approach is that many parts of the codebase now don't depend on the existence of `mitmproxy.ctx` and we can use off-the-shelf things like pytest's `caplog`. We can also now better colorize log output and/or add timestamps.

mitmproxy

Python

test_addonmanager.py

async def test_simple(caplog): with taddons.context(loadcore=False) as tctx: a = tctx.master.addons assert len(a) == 0 a.add(TAddon("one")) assert a.get("one") assert not a.get("two") assert len(a) == 1 a.clear() assert len(a) == 0 assert not a.chain with taddons.context(loadcore=False) as tctx: a.add(TAddon("one")) a.trigger("nonexistent") assert "AssertionError" in caplog.text f = tflow.tflow() a.trigger(hooks.RunningHook()) a.trigger(HttpResponseHook(f)) assert "not callable" in caplog.text caplog.clear() caplog.clear() a.get("one").response = addons a.trigger(HttpResponseHook(f)) assert "not callable" not in caplog.text a.remove(a.get("one")) assert not a.get("one") ta = TAddon("one") a.add(ta) a.trigger(hooks.RunningHook()) assert ta.running_called assert ta in a

c69239bb90c55993326c324908ac78cc2a174e44

https://github.com/mitmproxy/mitmproxy.git

async def test_simple(caplog): with taddons.context(loadcore=False) as tctx: a = tctx.master.addons assert len(a) == 0 a.add(TAddon("one")) assert a.get("one") assert not a.get("two") assert len(a) == 1 a.clear() assert len(a) == 0 assert not a.chain with taddons.context(loadcore=False) as tctx: a.add(TAddon("one")) a.trigger("nonexistent") assert "AssertionError" in caplog.text f = tflow.tflow()

test_simple

test/test_prototype_transforms_functional.py

cleanup prototype transforms functional tests (#6622) * cleanup prototype transforms functional tests * fix * oust local functions

vision

Python

test_prototype_transforms_functional.py

def test_scripted_smoke(self, info, args_kwargs, device): dispatcher = script(info.dispatcher) (image_feature, *other_args), kwargs = args_kwargs.load(device) image_simple_tensor = torch.Tensor(image_feature) dispatcher(image_simple_tensor, *other_args, **kwargs) # TODO: We need this until the dispatchers below also have `DispatcherInfo`'s. If they do, `test_scripted_smoke` # replaces this test for them.

658ca539369c80d8d609bf0e716dc9109186fbf4

https://github.com/pytorch/vision.git

def test_scripted_smoke(self, info, args_kwargs, device): dispatcher = script(info.dispatcher) (image_feature, *other_args), kwargs = args_kwargs.load(device) image_simple_tensor = torch.Tensor(image_feature) dispatcher(image_simple_tensor, *other_args, **kwargs) # TODO: We need this until the dispatchers below also have `DispatcherInfo`'s. If they do, `test_scripted_smoke` # replaces this te

test_scripted_smoke

awx/main/dispatch/control.py

Refactor canceling to work through messaging and signals, not database If canceled attempted before, still allow attempting another cancel in this case, attempt to send the sigterm signal again. Keep clicking, you might help! Replace other cancel_callbacks with sigterm watcher adapt special inventory mechanism for this too Get rid of the cancel_watcher method with exception in main thread Handle academic case of sigterm race condition Process cancelation as control signal Fully connect cancel method and run_dispatcher to control Never transition workflows directly to canceled, add logs

awx

Python

control.py

def cancel(self, task_ids, *args, **kwargs): return self.control_with_reply('cancel', *args, extra_data={'task_ids': task_ids}, **kwargs)

c59bbdecdbdd920c5d3d298d691129c6bbc94c5e

https://github.com/ansible/awx.git

def cancel(self, task_ids, *args, **kwargs): return self.control_with_reply('cancel', *args, extra_data={'task_ids': task_ids}, **kwargs)

cancel

django/forms/widgets.py

Refs #33476 -- Reformatted code with Black.

django

Python

widgets.py

def render_js(self): return [ format_html('<script src="{}"></script>', self.absolute_path(path)) for path in self._js ]

9c19aff7c7561e3a82978a272ecdaad40dda5c00

https://github.com/django/django.git

def render_js(self): return [ format_html('<script src="{}"></script>', self.absolute_path(path)) for path in self._js ]

render_js

erpnext/accounts/doctype/pos_invoice/pos_invoice.py

fix: POS properly validate stock for bundle products Stock availability was not calculated properly for Product Bundle with non stock item so i have added logic to properly calculate that as well.

erpnext

Python

pos_invoice.py

def get_bundle_availability(bundle_item_code, warehouse): product_bundle = frappe.get_doc("Product Bundle", bundle_item_code) bundle_bin_qty = 1000000 for item in product_bundle.items: item_bin_qty = get_bin_qty(item.item_code, warehouse) item_pos_reserved_qty = get_pos_reserved_qty(item.item_code, warehouse) available_qty = item_bin_qty - item_pos_reserved_qty max_available_bundles = available_qty / item.qty if bundle_bin_qty > max_available_bundles and frappe.get_value( "Item", item.item_code, "is_stock_item" ): bundle_bin_qty = max_available_bundles pos_sales_qty = get_pos_reserved_qty(bundle_item_code, warehouse) return bundle_bin_qty - pos_sales_qty

e392ea1104fee5add5c893c4e092edb6ad21f486

https://github.com/frappe/erpnext.git

def get_bundle_availability(bundle_item_code, warehouse): product_bundle = frappe.get_doc("Product Bundle", bundle_item_code) bundle_bin_qty = 1000000 for item in product_bundle.items: item_bin_qty = get_bin_qty(item.item_code, warehouse) item_pos_reserved_qty = get_pos_reserved_qty(

get_bundle_availability

src/sentry/runner/commands/sendmail.py

feat(runner): Add sendmail command to send emails from sentry command (#36732)

sentry

Python

sendmail.py

def sendmail(files, fail_silently): from sentry.runner import configure configure() for file in files: with open(file) as f: send_prepared_email(f.read(), fail_silently=fail_silently)

b72ff60d06f2cd1dae8390bebf71d191ae341faf

https://github.com/getsentry/sentry.git

def sendmail(files, fail_silently): fr

sendmail

modin/pandas/test/test_series.py

TEST-#5040: Rework test_series using eval_general() (#5041) Signed-off-by: Vasily Litvinov <fam1ly.n4me@yandex.ru>

modin

Python

test_series.py

def test_str_get(data, i): modin_series, pandas_series = create_test_series(data) eval_general(modin_series, pandas_series, lambda series: series.str.get(i)) @pytest.mark.parametrize( "data", test_string_list_data_values, ids=test_string_list_data_keys ) @pytest.mark.parametrize("sep", string_sep_values, ids=string_sep_keys)

d86dda5094eba47840f42a21cf4b2c953e698960

@pytest.mark.parametrize( "data", test_string_list_data_values, ids=test_string_list_data_keys ) @pytest.mark.parametrize("sep", string_sep_values, ids=string_sep_keys)

https://github.com/modin-project/modin.git

def test_str_get(data, i): modin_series, pandas_series = create_test_series(data) eval_general(modin_series, pandas_series, lambda series: series.str.get(i)) @pytest.mark.parametrize( "data", test_string_list_data_values, ids=test_string_list_data_keys ) @pytest.mark.parametrize("sep", string_sep_values, ids=string_sep_keys)

test_str_get

mindsdb/api/mysql/mysql_proxy/datahub/information_schema.py

return columns list for files

mindsdb

Python

information_schema.py

def _get_columns(self): columns = self.information_schema['COLUMNS'] # NOTE there is a lot of types in mysql, but listed below should be enough for our purposes row_templates = { 'text': ['def', 'SCHEMA_NAME', 'TABLE_NAME', 'COLUMN_NAME', 'COL_INDEX', None, 'YES', 'varchar', 1024, 3072, None, None, None, 'utf8', 'utf8_bin', 'varchar(1024)', None, None, 'select', None, None], 'timestamp': ['def', 'SCHEMA_NAME', 'TABLE_NAME', 'COLUMN_NAME', 'COL_INDEX', 'CURRENT_TIMESTAMP', 'YES', 'timestamp', None, None, None, None, 0, None, None, 'timestamp', None, None, 'select', None, None], 'bigint': ['def', 'SCHEMA_NAME', 'TABLE_NAME', 'COLUMN_NAME', 'COL_INDEX', None, 'YES', 'bigint', None, None, 20, 0, None, None, None, 'bigint unsigned', None, None, 'select', None, None], 'float': ['def', 'SCHEMA_NAME', 'TABLE_NAME', 'COLUMN_NAME', 'COL_INDEX', None, 'YES', 'float', None, None, 12, 0, None, None, None, 'float', None, None, 'select', None, None] } result = [] for table_name in self.information_schema: table_columns = self.information_schema[table_name] for i, column_name in enumerate(table_columns): result_row = row_templates['text'].copy() result_row[1] = 'information_schema' result_row[2] = table_name result_row[3] = column_name result_row[4] = i result.append(result_row) mindsb_dn = self.get('MINDSDB') for table_name in mindsb_dn.get_tables(): table_columns = mindsb_dn.get_table_columns(table_name) for i, column_name in enumerate(table_columns): result_row = row_templates['text'].copy() result_row[1] = 'mindsdb' result_row[2] = table_name result_row[3] = column_name result_row[4] = i result.append(result_row) mindsb_dn = self.get('FILES') for table_name in mindsb_dn.get_tables(): table_columns = mindsb_dn.get_table_columns(table_name) for i, column_name in enumerate(table_columns): result_row = row_templates['text'].copy() result_row[1] = 'files' result_row[2] = table_name result_row[3] = column_name result_row[4] = i result.append(result_row) df = pd.DataFrame(result, columns=columns) return df

284fb890eca785bec894e69c6d5b122e83ef93d0

https://github.com/mindsdb/mindsdb.git

def _get_columns(self): columns = self.information_schema['COLUMNS'] # NOTE there is a lot of types in mysql, but listed below should be enough for our purposes row_templates = { 'text': ['def', 'SCHEMA_NAME', 'TABLE_NAME', 'COLUMN_NAME', 'COL_INDEX', None, 'YES', 'varchar', 1024, 3072, None, None, None, 'utf8', 'utf8_bin', 'varchar(1024)', None, None, 'select', None, None], 'timestamp': ['def', 'SCHEMA_NAME', 'TABLE_NAME', 'COLUMN_NAME', 'COL_INDEX', 'CURRENT_TIMESTAMP', 'YES', 'timestamp', None, None, None, None, 0, None, None, 'timestamp', None, None, 'select', None, None], 'bigint': ['def', 'SCHEMA_NAME', 'TABLE_NAME', 'COLUMN_NAME', 'COL_INDEX', None, 'YES', 'bigint', None, None, 20, 0, None, None, None, 'bigint unsigned', None, None, 'select', None, None], 'float': ['def', 'SCHEMA_NAME', 'TABLE_NAME', 'COLUMN_NAME', 'COL_INDEX', None, 'YES', 'float', None, None, 12, 0, None, None, None, 'float', None, None, 'select', None, None] } result = [] for table_name in self.information_schema: table_columns = self.information_schema[table_name] for i, column_name in enumerate(table_columns): result_row = row_templates['text'].copy() result_row[1] = 'information_schema' result_row[2] = table_name result_row[3] = column_name result_row[4] = i result.append(result_row) mindsb_dn = self.get('MINDSDB') for table_name in mindsb_dn.get_tables(): table_columns = mindsb_dn.get_table_columns(table_name) for i, column_name in enumerate(table_columns): result_row = row_templates['text'].copy() resu

_get_columns

wagtail/core/migrations/0027_fix_collection_path_collation.py

Reformat with black

wagtail

Python

0027_fix_collection_path_collation.py

def set_collection_path_collation(apps, schema_editor): if schema_editor.connection.vendor == "postgresql": schema_editor.execute( )

d10f15e55806c6944827d801cd9c2d53f5da4186

https://github.com/wagtail/wagtail.git

def set_collection_path_collation(apps, schema_editor): if schema_editor.connection.vendor == "postgresql": schema_editor.execute( )

set_collection_path_collation

sklearn/manifold/tests/test_isomap.py

TST use global_dtype in sklearn/manifold/tests/test_isomap.py (#22673) Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> Co-authored-by: Jérémie du Boisberranger <jeremiedbb@users.noreply.github.com>

scikit-learn

Python

test_isomap.py

def test_pipeline_with_nearest_neighbors_transformer(global_dtype): # Test chaining NearestNeighborsTransformer and Isomap with # neighbors_algorithm='precomputed' algorithm = "auto" n_neighbors = 10 X, _ = datasets.make_blobs(random_state=0) X2, _ = datasets.make_blobs(random_state=1) X = X.astype(global_dtype, copy=False) X2 = X2.astype(global_dtype, copy=False) # compare the chained version and the compact version est_chain = pipeline.make_pipeline( neighbors.KNeighborsTransformer( n_neighbors=n_neighbors, algorithm=algorithm, mode="distance" ), manifold.Isomap(n_neighbors=n_neighbors, metric="precomputed"), ) est_compact = manifold.Isomap( n_neighbors=n_neighbors, neighbors_algorithm=algorithm ) Xt_chain = est_chain.fit_transform(X) Xt_compact = est_compact.fit_transform(X) assert_allclose(Xt_chain, Xt_compact) Xt_chain = est_chain.transform(X2) Xt_compact = est_compact.transform(X2) assert_allclose(Xt_chain, Xt_compact) @pytest.mark.parametrize( "metric, p, is_euclidean", [ ("euclidean", 2, True), ("manhattan", 1, False), ("minkowski", 1, False), ("minkowski", 2, True), (lambda x1, x2: np.sqrt(np.sum(x1**2 + x2**2)), 2, False), ], )

35b826a49c66bc6cb03dc751784483806aba6400

@pytest.mark.parametrize( "metric, p, is_euclidean", [ ("euclidean", 2, True), ("manhattan", 1, False), ("minkowski", 1, False), ("minkowski", 2, True), (lambda x1, x2: np.sqrt(np.sum(x1**2 + x2**2)), 2, False), ], )

https://github.com/scikit-learn/scikit-learn.git

def test_pipeline_with_nearest_neighbors_transformer(global_dtype): # Test chaining NearestNeighborsTransformer and Isomap with # neighbors_algorithm='precomputed' algorithm = "auto" n_neighbors = 10 X, _ = datasets.make_blobs(random_state=0) X2, _ = datasets.make_blobs(random_state=1) X = X.astype(global_dtype, copy=False) X2 = X2.astype(global_dtype, copy=False) # compare the chained version and the compact version est_chain = pipeline.make_pipeline(

test_pipeline_with_nearest_neighbors_transformer

ppocr/data/imaug/fce_aug.py

add fcenet

PaddleOCR

Python

fce_aug.py

def generate_crop_target(self, image, all_polys, pad_h, pad_w): h, w, _ = image.shape h_array = np.zeros((h + pad_h * 2), dtype=np.int32) w_array = np.zeros((w + pad_w * 2), dtype=np.int32) text_polys = [] for polygon in all_polys: rect = cv2.minAreaRect(polygon.astype(np.int32).reshape(-1, 2)) box = cv2.boxPoints(rect) box = np.int0(box) text_polys.append([box[0], box[1], box[2], box[3]]) polys = np.array(text_polys, dtype=np.int32) for poly in polys: poly = np.round(poly, decimals=0).astype(np.int32) minx = np.min(poly[:, 0]) maxx = np.max(poly[:, 0]) w_array[minx + pad_w:maxx + pad_w] = 1 miny = np.min(poly[:, 1]) maxy = np.max(poly[:, 1]) h_array[miny + pad_h:maxy + pad_h] = 1 h_axis = np.where(h_array == 0)[0] w_axis = np.where(w_array == 0)[0] return h_axis, w_axis

9f62b610dea6161627200ed85d92e19b1923279a

https://github.com/PaddlePaddle/PaddleOCR.git

def generate_crop_target(self, image, all_polys, pad_h, pad_w): h, w, _ = image.shape h_array = np.zeros((h + pad_h * 2), dtype=np.int32) w_array = np.zeros((w + pad_w * 2), dtype=np.int32) text_polys = [] for polygon in all_polys: rect = cv2.minAreaRect(polygon.astype(np.int32).reshape(-1, 2)) box = cv2.boxPoints(rect) box = np.int0(box) text_polys.append([box[0], box[1], box[2], box[3]]) polys = np.array(text_polys, dtype=np.int32) for poly in polys: poly = np.round(poly, decimals=0).astype(np.int32) minx = np.min(poly[:, 0]) maxx = np.max(poly[:, 0]) w_array[minx + pad_w:maxx + pad_w] = 1 miny = np.min(po

generate_crop_target

netbox/dcim/views.py

Fixes #8935: Correct ordering of next/previous racks to use naturalized names

netbox

Python

views.py

def get_extra_context(self, request, instance): # Get 0U devices located within the rack nonracked_devices = Device.objects.filter( rack=instance, position__isnull=True, parent_bay__isnull=True ).prefetch_related('device_type__manufacturer') peer_racks = Rack.objects.restrict(request.user, 'view').filter(site=instance.site) if instance.location: peer_racks = peer_racks.filter(location=instance.location) else: peer_racks = peer_racks.filter(location__isnull=True) next_rack = peer_racks.filter(_name__gt=instance._name).first() prev_rack = peer_racks.filter(_name__lt=instance._name).reverse().first() reservations = RackReservation.objects.restrict(request.user, 'view').filter(rack=instance) power_feeds = PowerFeed.objects.restrict(request.user, 'view').filter(rack=instance).prefetch_related( 'power_panel' ) device_count = Device.objects.restrict(request.user, 'view').filter(rack=instance).count() return { 'device_count': device_count, 'reservations': reservations, 'power_feeds': power_feeds, 'nonracked_devices': nonracked_devices, 'next_rack': next_rack, 'prev_rack': prev_rack, }

197dfca5b2d181369b90e40704ac9188d149a688

https://github.com/netbox-community/netbox.git

def get_extra_context(self, request, instance): # Get 0U devices located within the rack nonracked_devices = Device.objects.filter( rack=instance, position__isnull=True, parent_bay__isnull=True ).prefetch_related('device_type__manufacturer') peer_racks = Rack.objects.restrict(request.user, 'view').filter(site=instance.site) if instance.location: peer_racks = peer_racks.filter(location=instance.location) else: peer_racks = peer_racks.filter(location__isnull

get_extra_context

tests/integration_tests/test_ray.py

int: Refactor `test_ray.py` to limit number of full train jobs (#2637) * refactors majority of ray tests to compare preprocessing results * reverted changes to concatenate_datasets; use fixed split config * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * added partial modin fix; fixed reshape guard Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Justin Zhao <justinxzhao@gmail.com>

ludwig

Python

test_ray.py

def test_ray_tabular(tmpdir, df_engine, ray_cluster_2cpu): input_features = [ category_feature(encoder={"vocab_size": 2}, reduce_input="sum"), number_feature(normalization="zscore"), set_feature(), binary_feature(), bag_feature(), h3_feature(), date_feature(), ] output_features = [ binary_feature(bool2str=["No", "Yes"]), binary_feature(), number_feature(normalization="zscore"), ] run_preprocessing( tmpdir, df_engine, input_features, output_features, ) @pytest.mark.parametrize("dataset_type", ["csv", "parquet"]) @pytest.mark.distributed

0b33b3f6524ecbf3671b2c911efe429feb93988f

@pytest.mark.parametrize("dataset_type", ["csv", "parquet"]) @pytest.mark.distributed

https://github.com/ludwig-ai/ludwig.git

def test_ray_tabular(tmpdir, df_engine, ray_cluster_2cpu): input_features = [ category_feature(encoder={"vocab_size": 2}, reduce_input="sum"), number_feature(normalization="zscore"), set_feature(), binary_feature(), bag_feature(), h3_feature(), date_feature(), ] output_features = [ binary_

test_ray_tabular

src/paperless_tika/parsers.py

improve pdf generation

paperless-ngx

Python

parsers.py

def extract_metadata(self, document_path, mime_type): result = [] prefix_pattern = re.compile(r"(.*):(.*)") try: parsed = self.get_tika_result(document_path) except ParseError as e: self.log( "warning", f"Error while fetching document metadata for " f"{document_path}: {e}", ) return result for key, value in parsed["metadata"].items(): if isinstance(value, list): value = ", ".join([str(e) for e in value]) value = str(value) try: m = prefix_pattern.match(key) result.append( { "namespace": "", "prefix": m.group(1), "key": m.group(2), "value": value, }, ) except AttributeError: result.append( { "namespace": "", "prefix": "", "key": key, "value": value, }, ) except Exception as e: self.log( "warning", f"Error while reading metadata {key}: {value}. Error: " f"{e}", ) result.sort(key=lambda item: (item["prefix"], item["key"])) return result

c8081595c4450780eade4921a81d0b1bd08105cc

https://github.com/paperless-ngx/paperless-ngx.git

def extract_metadata(self, document_path, mime_type): result = [] prefix_pattern = re.compile(r"(.*):(.*)") try: parsed = self.get_tika_result(document_path) except ParseError as e: self.log( "warning", f"Error while fetching document metadata for " f"{document_path}: {e}", ) return result for key, value in parsed["metadata"].items(): if isinstance(value, list): value = ", ".join([str(e) for e in value]) value = str(value) try: m = prefix

extract_metadata

lib/matplotlib/axis.py

DOC: improve grammar and consistency

matplotlib

Python

axis.py

def update_units(self, data): converter = munits.registry.get_converter(data) if converter is None: return False neednew = self.converter != converter self.converter = converter default = self.converter.default_units(data, self) if default is not None and self.units is None: self.set_units(default) elif neednew: self._update_axisinfo() self.stale = True return True

9b6abd0b4933811e0a45c2535ab8fd107db65dd9

https://github.com/matplotlib/matplotlib.git

def update_units(self, data): converter = munits.registry.get_converter(data) if converter is None:

update_units

wagtail/snippets/views/snippets.py

Fix missing preview_url_name in SnippetViewSet.revisions_revert_view (#9663)

wagtail

Python

snippets.py

def revisions_revert_view(self): return self.revisions_revert_view_class.as_view( model=self.model, permission_policy=self.permission_policy, index_url_name=self.get_url_name("list"), edit_url_name=self.get_url_name("edit"), delete_url_name=self.get_url_name("delete"), history_url_name=self.get_url_name("history"), preview_url_name=self.get_url_name("preview_on_edit"), revisions_revert_url_name=self.get_url_name("revisions_revert"), )

baddbfad424afa797287151e6b04d9c0459df3ef

https://github.com/wagtail/wagtail.git

def revisions_revert_view(self): return self.revisions_revert_view_class.as_view( model=self.model, permission_policy=self.permission_policy, index_url_name=self.get_url_name("list"), edit_url_name=self.get_url_name("edit"), delete_url_name=self.get_url_name("delete"), history_url_name=self.get_url_name("history"), preview_url_name=self.get_url_name("preview_on_edit"), revisions_revert_u

revisions_revert_view